Computer-implemented systems and methods for resource allocation

ABSTRACT

Systems and methods are described for processing queue data and for providing queue messaging over a network. An illustrative queuing system includes a first queue configured to hold resource requests from a plurality of users, and program code stored in computer readable memory configured to determine or estimate whether a resource requested by a first resource request submitted by a first requester will be available when the first resource request will be serviced, and to transmit a message over a network to the first requester indicating that the requested resource will not be available when the queued request is serviced if it is estimated or determined that the requested resource will not be available when the first request is serviced.

PRIORITY CLAIM

This is a continuation of U.S. patent application Ser. No. 11/386,459(filed 22 Mar. 2006), which claims the benefit of U.S. ProvisionalPatent Application 60/663,999 (filed 22 Mar. 2005), U.S. ProvisionalPatent Application 60/664,000 (filed 22 Mar. 2005), U.S. ProvisionalPatent Application 60/664,028 (filed 22 Mar. 2005), U.S. ProvisionalPatent Application 60/664,131 (filed 22 Mar. 2005), and U.S. ProvisionalPatent Application 60/664,234 (filed 22 Mar. 2005). The entiredisclosure of all of these priority applications is hereby incorporatedby reference herein in its entirety.

COPYRIGHT RIGHTS

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by any one of the patentdocument or the patent disclosure, as it appears in the Patent andTrademark Office patent file or records, but otherwise reserves allcopyright rights whatsoever.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is related to resource allocation, and inparticular, to apparatus and processes for electronically allocatingresources and for providing queue messaging over a network related toresource requests.

2. Description of the Related Art

Conventional systems for allocating resources often do not efficientlyallocate such resources or units to users. There have been severalconventional approaches to servicing requests for and servicing resourcerequests, where finite resources are available, and where the potentialdemand may exceed the available resources. However, disadvantageously,many of these conventional approaches offer limited flexibility inallocating resources. Still further, certain approaches maydisadvantageously result in too few resources being distributed. Stillfurther, certain embodiments appear to result in an inequitabledistribution of resources.

SUMMARY OF THE INVENTION

Embodiments described herein are related to resource allocation, and inparticular, to apparatus and processes for electronically allocatingfinite or limited resources and/or for providing queue messaging over anetwork related to resource requests. The following example embodimentsand variations thereof are illustrative examples, and are not intendedto limit the scope of the invention.

One embodiment includes a queuing system comprising: a first queueconfigured to hold resource requests from a plurality of users; programcode stored in computer readable memory configured: to determine orestimate whether a resource requested by a first resource requestsubmitted by a first requester will be available when the first resourcerequest will be serviced; and to transmit a message over a network tothe first requester indicating that the requested resource will not beavailable when the queued request is serviced if it is estimated ordetermined that the requested resource will not be available when thefirst request is serviced.

The system optionally further comprises program code stored in computerreadable memory configured to automatically identify to the firstrequester a potential alternate resource at least partly in response todetermining or estimating the requested resource will not be availablewhen the first request is serviced, program code stored in computerreadable memory configured to automatically identify an alternateresource that may be acceptable to the first requester at least partlyin response to determining or estimating the requested resource will notbe available when the queued request is serviced, and/or program codestored in computer readable memory configured to transmit to therequester an estimated time period related to when the resource requestwill be serviced. The requested resource can relate to tickets, such asseat or event tickets, by way of example.

One embodiment provides a method of providing queue messaging toresource requesters, the method comprising: determining or estimating aposition in a queue of a resource request associated with a firstrequester; determining or estimating whether the requested resource willbe available when the queued request is serviced; and if it is estimatedor determined that the requested resource will not be available when thequeued request is serviced, transmitting a message over a network to thefirst requester indicating that the requested resource will not beavailable when the queued request is serviced. Optionally, the methodfurther comprises automatically identifying to the first requester apotential alternate resource at least partly in response to determiningor estimating the requested resource will not be available when thequeued request is serviced. Optionally, the method further comprisesusing filtering to automatically identify an alternate resource that maybe acceptable to the first requester at least partly in response todetermining or estimating the requested resource will not be availablewhen the queued request is serviced. Optionally, the method furthercomprises transmitting to the requester an estimated time period relatedto when the resource request will be serviced. The requested resourcecan relate to tickets, such as seat or event tickets, by way of example.

An example embodiment provides a method of setting a ticket price, themethod comprising: storing in computer readable memory an estimate of afirst population size of potential attendees of a first event at avenue, the first population including at least a first group and asecond group, wherein members of the first group have a first motivationfor attending the first event, and members of the second group have asecond motivation for attending the first event; storing in computerreadable memory an estimate of the size of the first group; storing incomputer readable memory an estimate of a first price threshold that itis estimated would result in at least a first percentage of members ofthe first group purchasing an event ticket; storing in computer readablememory an estimate of the size of the second group; storing in computerreadable memory an estimate of a second price threshold that it isestimated would result in at least a second percentage of members of thesecond group purchasing an event ticket; storing in computer readablememory information identifying a plurality of venue seating areas;accessing from computer readable memory a first limit on how manytickets a user can purchase in at least one of the venue seating areas;and based at least in part on the size of the first group, the size ofthe second group, the first price threshold, the second price threshold,and the first limit, setting a least a first event ticket price.

With reference to the example above, in an example embodiment, the firstmotivation is to impress others. By way of further example, the membersof the first group will tend to only purchase relatively expensive eventtickets. By way of example, the members of the first group will tend toonly purchase relatively inexpensive event tickets. By way of furtherexample, the members of the first group have relatively high incomes. Byway of still further example, the members of the first group haverelatively low incomes. By way of yet further example, the members ofthe first group include ticket brokers. Optionally, the method furthercomprises accessing a first parameter associated with patience withrespect to ticket acquisition associated with the first group. In anexample embodiment, the second motivation is related to an interest inthe event. By way of further example, the second motivation is relatedto socializing. Optionally, the method further comprises accessing afirst minimum ticket price, wherein the first minimum price is set basedat least in part on a first revenue guarantee to a first entity.

An example embodiment provides a method of setting a ticket price, themethod comprising: storing in computer readable memory a first sizeestimate corresponding to a first set of potential event attendees,wherein members of the first set are characterized by a first level ofincome, and/or a first parameter associated with patience with respectto ticket acquisition; storing in computer readable memory a second sizeestimate corresponding to a second set of potential event attendees,wherein members of the second set are characterized by a second level ofincome and/or a second parameter associated with patience with respectto ticket acquisition; storing in computer readable memory a firstindication as to what event ticket price at least a portion of membersof the first set would be willing to pay; storing in computer readablememory a second indication as to what event ticket price at least aportion of members of the second set would be willing to pay; and basedat least in part on the first size estimate, the second size estimate,the first indication, and the second indication, setting a least a firstevent ticket price.

Optionally, the method above further comprises accessing event costdata, wherein the first event ticket price is set partly based on theevent cost data. Optionally, the method above further comprisesaccessing historical ticket sales data related to resold tickets,wherein the first event ticket price is set partly based on thehistorical ticket sales data related to resold tickets. Optionally, themethod above further comprises accessing a median ticket price that itis estimated that at least a first portion of the first set would bewilling to pay, wherein the first event ticket price is set partly basedon the median ticket price. Optionally, the method above furthercomprises accessing historical event-related income data, wherein thefirst event ticket price is set partly based on the historicalevent-related income data.

An example embodiment provides a method of setting a ticket price, themethod comprising reading a first limit on how many event tickets a usercan purchase, wherein the first event ticket price is set partly basedon the first limit.

An example embodiment provides a method of setting a ticket price, themethod comprising reading a first minimum permissible event ticketprice, wherein the first event ticket price is set partly based on thefirst minimum permissible ticket price.

An example embodiment provides a method of setting a ticket price, themethod comprising: storing in computer readable memory a first sizeestimate corresponding to a first set of potential event attendees,wherein members of the first set are characterized by a first level ofincome; storing in computer readable memory a second size estimatecorresponding to a second set of potential event attendees, whereinmembers of the second set are characterized by a second level of income;storing in computer readable memory a first indication as to what eventticket price at least a portion of members of the first set would bewilling to pay; storing in computer readable memory a second indicationas to what event ticket price at least a portion of members of thesecond set would be willing to pay; and based at least in part on thefirst size estimate, the second size estimate, the first indication, andthe second indication, setting a least a first event ticket price.

Optionally, the members of the first set are associated with a firstmotivation for attending the event. In certain instances, members of thefirst set are likely to purchase relatively expensive event seats toimpress others. In certain instances, members of the first set arelikely to attend the event with the intention of socializing withothers. In certain instances, members of the first set are likely toonly purchase relatively inexpensive event tickets. In certaininstances, members of the first set have relatively high incomes and/ora relatively large amount of disposable income. In certain instances,members of the first set have relatively low incomes. In certaininstances, members of the first set include ticket brokers that reselltickets. The above method optionally further comprises: accessing afirst parameter associated with patience with respect to ticketacquisition associated with members of the first set, wherein the firstevent ticket price is set partly based on the first parameter. The abovemethod optionally further comprises reading a first limit on how manyevent tickets a user can purchase, wherein the first event ticket priceis set partly based on the first limit. The above method optionallyfurther comprises reading a first minimum permissible event ticketprice, wherein the first event ticket price is set partly based on thefirst minimum permissible ticket price. The above method optionallyfurther comprises accessing historical ticket sales data, wherein thefirst event ticket price is set partly based on the historical ticketsales data. The above method optionally further comprises accessingevent cost data, wherein the first event ticket price is set partlybased on the event cost data. The above method optionally furthercomprises accessing historical ticket sales data related to resoldtickets, wherein the first event ticket price is set partly based on thehistorical ticket sales data related to resold tickets. The above methodoptionally further comprises accessing a median ticket price that it isestimated that at least a first portion of the first set would bewilling to pay, wherein the first event ticket price is set partly basedon the median ticket price. The above method optionally furthercomprises accessing historical sales data related to concessions sold atleast one event, wherein the first event ticket price is set partlybased on the historical sales data related to concessions. The abovemethod optionally further comprises accessing historical sales datarelated to event income, wherein the first event ticket price is setpartly based on the historical income data. The above method optionallyfurther comprises setting the first event ticket price is set partlybased on the historical sales data related to concessions. The abovemethod optionally further comprises setting ticket prices for aplurality of venue areas at least partly based on historical sales datarelated to concessions and tickets. The above method optionally furthercomprises accessing cost data related to putting on the event, whereinthe first event ticket price is set partly based on the cost data.

An example embodiment provides a method of processing electronic queuedata, the method comprising: determining or estimating the number ofticket-related requests that are in a first electronic queue; based atleast in part on the number of ticket-related requests that are in afirst electronic queue and historical queue abandonment data, selectinga notification-type regarding the queue to be provided to at least oneuser that has a ticket-related request in the queue; and transmitting anotification corresponding to the notification-type to a terminalassociated with at least one user that has a ticket-related request inthe queue.

With reference to the example embodiment above, in certain instances,the notification-type is selected to reduce the number of users thatabandon their queued ticket-related request. In certain instances, theselected notification-type is used to present an estimated queue waittime rounded up to the nearest minute. In certain instances, theselected notification-type is used to present a queue wait time as beingless than a specified time duration. In certain instances, the selectednotification-type is used to present a notification regarding the queuethat does not include queue wait time information. In certain instances,the selected notification-type is selected partly based on an historicalaverage and/or standard deviation of length of time one or more userswere willing to wait in queue before abandoning their queuedticket-related requests.

An example embodiment provides a method of processing electronic queuedata, the method comprising: accessing historical queue abandonment datafor at least one ticket queue, wherein the abandonment data relates tousers abandoning queued ticket-related requests; based at least in parton the historical queue abandonment data, selecting a type of queueinformation that is to be provided to at least one user that has aqueued ticket-related request; and transmitting a notification includinginformation corresponding to the selected the type of queue informationto a terminal associated with at least one user that has a queuedticket-related request.

With reference to the example embodiment above, in certain instances,the type of queue information is selected to reduce queue abandonment.In certain instances, the type of queue information is selected toincrease queue abandonment. In certain instances, the selected type ofqueue information includes an estimated queue wait time rounded up tothe nearest minute. In certain instances, the notification presents aqueue wait time as being less than a specified time duration. In certaininstances, the selected type of queue information does not include queuewait time information. In certain instances, the type of queueinformation is selected partly based on an historical average and/orstandard deviation of length of time one or more users were willing towait in a queue before abandoning their queued ticket-related requests.In certain instances, the ticket-related request is a request topurchase at least one ticket. In certain instances, the ticket-relatedrequest is a request to view ticket availability.

An example embodiment provides a method of processing electronic queuedata, the method comprising: receiving in a queue a ticket-relatedrequest for a first event from a first user; determining seatavailability for the first event; based at least in part on the actual,estimated, or relative position of the ticket-related request in thequeue and the seat availability, estimating whether seats will beavailable when the first user's ticket-related request is serviced; andproviding an indication to the user that seats will not be availablewhen the estimate indicates seats will not be available when the firstuser's ticket-related request is serviced.

With reference to the example embodiment above, in certain instances,servicing the ticket-related request includes determining if there areseats available that correspond to the request. The above methodoptionally further comprises providing the first user with informationregarding a second event, wherein a same performer is scheduled to be atboth the first event and the second event. The above method optionallyfurther comprises providing the first user with information regarding asecond event at least partly in response to the estimate indicatingseats will not be available when the first user's ticket-related requestis serviced. The above method optionally further comprises providing thefirst user with information regarding a second event at least partly inresponse to the estimate indicating seats will not be available when thefirst user's ticket-related request is serviced, wherein the secondevent is selected at least partly using collaborative filtering. Theabove method optionally further comprises receiving in a queue aticket-related request for a first event from a first user, and based atleast in part on a actual, estimated, or relative position of theticket-related request in the queue and event seat availability,selectively indicating to the first user that seats will not be or areunlikely to be available when the first user's ticket-related request isserviced. In certain instances, the ticket-related request in theexample embodiment includes determining if there are seats availablethat correspond to the request. The above method optionally furthercomprises providing the first user with information regarding a secondevent at least partly in response to estimating or determining thatseats will not be available when the first user's ticket-related requestis serviced, wherein a same performer is scheduled to be at both thefirst event and the second event. The above method optionally furthercomprises providing the first user with information regarding a secondevent at least partly in response to estimating or determining thatseats will not be available when the first user's ticket-related requestis serviced, wherein the information regarding the second eventidentifies a performer for the second event, wherein the identifiedperformer is not scheduled to perform at the first event. The abovemethod optionally further comprises providing the first user withinformation regarding a second event at least partly in response toestimating or determining that seats will not be available when thefirst user's ticket-related request is serviced, wherein the secondevent is selected at least partly using collaborative filtering.

An example embodiment provides a method of providing access to tickets,the method comprising: transmitting over a network to a first user anoffer to participate in a first phase of a ticket sale for a first eventfor a first participation fee; receiving over the network at a ticketingsystem an indication that the offer has been accepted by the first user;during the first phase of the ticket sale, receiving over the network aticket request from the first user; and at least partly in response toreceiving the ticket request, determining that the first user isentitled to participate in the first phase of the ticket sale.

The above method optionally further comprises determining how many seatsare to be made available during the first phase based at least in parton the number of users that paid the first participation fee. In certaininstances, the first participation fee is not refundable. In certaininstances, the first participation fee is refundable if the first userdoes not purchase a ticket during the first phase. The above methodoptionally further comprises transmitting over the network an offer toparticipate in a second phase of the ticket sale for the first event fora second participation fee, wherein the second phase takes place afterthe first phase, and the second participation fee is lower than thefirst participation fee. The above method optionally further comprisesenabling a plurality of users to participate in another phase of theevent ticket sale without paying a participation fee. Optionally, incertain instances, only certain users are entitled to pay theparticipation fee so as to participate in the first phase of the ticketsale. Optionally, in certain instances, only members of a fan clubassociated with a performer at the first event. Optionally, in certaininstances, only holders of a first credit card-type are entitled to paythe first participation fee.

An example embodiment provides a method of providing access to tickets,the method comprising: transmitting over a network to a first user anoffer to participate in a first phase of a ticket sale for a first eventin exchange for a first premium provided by the first user; receivingover the network at a ticketing system an indication that the offer hasbeen accepted by the first user; during the first phase of the ticketsale, receiving over the network a ticket-related request from the firstuser; and at least partly in response to receiving the ticket-relatedrequest, determining that the first user is entitled to participate inthe first phase of the ticket sale.

The above method optionally further comprises providing the first userwith a password or a link via which the first user can provide anindication that the first user is entitled to participate in the firstphase of the ticket sale. Optionally, in certain instances, tickets soldduring the first phase are sold at a fixed ticket price. Optionally, incertain instances, tickets sold during the first phase are sold atauction. Optionally, the method further comprises determining how manyseats are to be made available during the first phase based at least inpart on the number of users that paid the first premium. Optionally, incertain instances, the first premium is not refundable. Optionally, incertain instances, the first premium is refundable if the first userdoes not purchase a ticket during the first phase. Optionally, themethod further comprises transmitting over the network an offer toparticipate in a second phase of the ticket sale for the first event fora second premium, wherein the second phase takes place after the firstphase, and the second premium is different than the first premium.Optionally, the method further comprises enabling a plurality of usersto participate in another phase of the event ticket sale withoutproviding a premium. Optionally, in certain instances, only certainusers are entitled to provide the premium so as to participate in thefirst phase of the ticket sale. Optionally, in certain instances, onlymembers of a fan club associated with a performer at the first event areentitled to provide the first premium so as to participate in the firstphase. Optionally, in certain instances, only holders of a first creditcard-type are entitled to provide the first premium so as to participatein the first phase.

An example embodiment provides a method of dynamically setting eventticket prices, comprising setting an initial seat ticket price for afirst event based at least in part on historical ticket sales data andon historical event-related income data; monitoring ticket sales datafor the first event and storing ticket sales data in computer readable;and setting a second seat ticket price after a ticket sale for the eventhas begun based at least in part on ticket sales data for the firstevent, on historical ticket sales data for at least one previous event,and on a specified price adjustment limit that indicates how much thesecond seat ticket price can vary from the initial seat ticket price.

The above method optionally further comprises timing a sales offer ofseat tickets at the second seat ticket price in accordance with apredetermined schedule stored in computer readable memory. Optionally,the method further comprises setting the initial seat ticket pricepartly based on event cost data. In certain instances, the historicalevent-related income data includes historical parking revenues or otherconcession-related revenues. Optionally, the method further comprisessetting the initial seat ticket price partly based on event cost data,accessing from computer readable memory an indication as to the maximumnumber of seat ticket price adjustments that can be performed for thefirst event. In certain instances, the setting of the second seat ticketprice is based at least in part on unsold seat inventory. Optionally,the method further comprises timing a sales offer of seat tickets at thesecond seat ticket price is based at least in the time period remainingprior to the event. Optionally, the method further comprisestransmitting an email, an SMS message, and/or an instant messagedirected to at least one person providing information to the personrelated to the setting of the second ticket price. Optionally, themethod further comprises releasing additional seats for which ticketsmay be procured at a time after the beginning of the event ticket saleand before the event occurring. Optionally, the method further comprisesreleasing additional seats for which tickets may be procured at a timeafter the beginning of the event ticket sale and before the eventoccurring, determining that at least one user requested that anotification be when additional seats are released, and causing anotification regarding the release of additional seats to be transmittedto the at least one user. In certain instances, the initial seat ticketprice is partly selected so as to be high enough so as to reduce thenumber of seat tickets purchased by a given ticket purchaser so as toreduce the number of orphaned event seats. In certain instances, theinitial seat ticket price and/or the second seat price is partlyselected so as to reduce the number of orphaned event seats.

An example embodiment provides a method of dynamically setting eventticket prices: setting an initial seat ticket price for a first eventbased at least in part on historical ticket sales data; storing incomputer readable memory a schedule of at least one seat ticket pricereduction; and transmitting over a network to a terminal associated witha potential ticket purchaser the schedule of the at least one seatticket price reduction.

The above method optionally further comprises transmitting the amount ofthe price reduction to the terminal. Optionally, the method furthercomprises transmitting to the terminal in association with the seatprice reduction schedule: an event name; an event date; anidentification of at least one available seat and the currentcorresponding seat price; and a limit on the quantity of tickets thepotential ticket purchaser can purchase for the event. In certaininstances, the transmitted schedule includes a date for the at least oneseat ticket price reduction. In certain instances, the transmittedschedule includes a time for the at least one seat ticket pricereduction. In certain instances, the amount of the at least one seatticket price reduction is selected based at least in part on historicalticket sales data. In certain instances, the initial seat ticket priceis set based at least in part on historical event-related income data.Optionally, the method further comprises transmitting an email, an SMSmessage, and/or an instant message to at least one person notifying theperson of the ticket price reduction. Optionally, the method furthercomprises releasing additional seats for which tickets may be procuredat a time after the beginning of the event ticket sale and before theevent occurring. Optionally, the method further comprises releasingadditional seats for which tickets may be procured at a time after thebeginning of the event ticket sale and before the event occurring,determining that at least one user requested that a notification beprovided to the at least one user when additional seats are released,and causing a notification regarding the release of additional seats tobe transmitted to the at least one user.

An example embodiment provides a method of dynamically setting eventticket prices: storing information related to a desired pattern ofticket sales over time for a first event; monitoring actual ticket salesover time for the event; and setting at least a first seat ticket pricebased at least in part on the desired pattern and the actual ticketsales over time for the event, wherein the first seat ticket price isset after the beginning of a ticket sale for the event.

In certain instances, the desired pattern is based at least in part onactual ticket sales over time for at least one historical event. Theabove method optionally further comprises transmitting to a terminalassociated with a potential purchaser: an event name; an event date; anidentification of at least one available seat; the first seat ticketprice; and a limit on the quantity of tickets the potential ticketpurchaser can purchase for the event. In certain instances, an initialseat ticket price is set prior to the setting of the first seat ticketprice based at least in part on historical concession sales data. Incertain instances, an initial seat ticket price is set prior to thesetting of the first seat ticket price based at least in part onhistorical income data related to one or more past events. The abovemethod optionally further comprises transmitting an email, an SMSmessage, and/or an instant message to at least one person notifying theperson of the availability of seat tickets at the first ticket price.The above method optionally further comprises releasing seats for whichtickets may be procured at the first ticket price.

An example embodiment provides a method of setting minimum bids fortickets in a ticket auction, the method comprising: setting an initialminimum bid for a first set of seat tickets for a first event based atleast in part on historical sales data; and setting a minimum bid for asecond set of seat tickets for the first event based at least in part onbid information for the first event, wherein the second set of seattickets are auctioned after the first set of seat tickets.

In certain instances of the above embodiment, the bid informationincludes information related to the number of bids received for ticketsfor the first event. In certain instances, the bid information includesinformation related to the rate of bids for tickets for the first event.In certain instances, the historical sales data includes historicalparking revenues. In certain instances, the historical sales dataincludes historical concession revenues. In certain instances, thehistorical sales data includes historical ticket sales data. The abovemethod optionally further comprises reading from computer readablememory a minimum bid adjustment limit. The above method optionallyfurther comprises reading from computer readable memory a first minimumbid adjustment limit, wherein the setting of the minimum bid for asecond set of seat tickets is constrained by the first minimum bidadjustment limit. In certain instances, the setting of the secondminimum bid is based at least in part on unsold seat inventory. Incertain instances, the timing of offering at auction seat tickets withassociated with the second minimum bid is based at least in the timeperiod remaining prior to the event. The above method optionally furthercomprises releasing additional seat tickets at a time after thebeginning of the event ticket auction and before the event occurring,determining that at least one user requested that a notification beprovided when additional seats are released for auction, and causing anotification related the release of additional seats to be transmittedto the at least one user. The above method optionally further comprisesstoring a reserve price associated with at least one seat ticket beingauctioned, wherein the reserve price is not disclosed to bidders priorto the close of the auction for the at least one seat ticket. The abovemethod optionally further comprises determining winning bids for ticketsfor a first set of seats, wherein the winning bids include a highestwinning bid from a first bidder and a lowest winning bid from a secondbidder, calculating a first ticket price lower than the highest winningbids and higher than the lowest winning bid, charging the first bidderthe first ticket price for at least one ticket, and charging the secondbidder the first ticket price for at least one ticket. The above methodoptionally further comprises charging the first bidder a delivery and/orservice fee in addition to the first ticket price.

An example embodiment provides a method of setting ticket prices for anevent, the method comprising: causing a survey related to ticket pricingto be provided over a network to at least a first user, wherein thesurvey asks the first user to provide an indication as to the value ofseats in a plurality of venue areas; receiving survey results, includingsurvey responses and/or information derived from survey responses;accessing historical ticket sales data stored in computer readablememory; accessing historical income data stored in computer readablememory; accessing cost data associated with providing the event storedin computer readable memory; and based at least in part on the surveyresults, the historical ticket sales data, and the cost data, setting atleast a first ticket price and/or a first minimum bid amount.

In certain instances, the survey requests that the first user provide amonetary value for at least a ticket for a seat in a first venue areaand for a ticket for a seat in a second venue area. In certaininstances, the survey requests that the first user provide an indicationas to the first user's opinion on the difference in quality of a firstseat in a first venue area and a second seat in a second venue area. Incertain instances, the survey is provided to the first user prior totickets for the event being offered for sale. In certain instances, thesurvey is provided to the first user during a sale of event tickets,wherein the sale is restricted to a subset of potential ticketpurchasers. In certain instances, the survey is provided to the firstuser during a sale of event tickets, wherein the sale is open to thegeneral public. In certain instances, the survey is provided to thefirst user during a ticket selection or ticket purchase process. Incertain instances, the survey requests that the first user provide seatrankings for a plurality of seats and/or seating areas. In certaininstances, the survey requests that the first user provide a rankingand/or a valuation for a specified row. In certain instances, the surveyrequests that the first user provide a ranking and/or a valuation for aspecified portion of a row.

An example embodiment provides a method of selecting ticket prices foran event, the method comprising: causing a survey related to seatranking and/or valuations to be provided over a network to at least afirst user, wherein the survey asks the first user to provide anindication as to the value and/or ranking of seats in a plurality ofvenue areas; receiving survey results, including survey responses and/orinformation derived from survey responses; accessing historical salesdata stored in computer readable memory; based at least in part on thesurvey results and historical ticket sales data, setting at least afirst ticket price and/or a first minimum bid amount.

In certain instances, the survey requests that the first user provide amonetary value for a first seat ticket in a first venue area and for asecond seat ticket in a second venue area. In certain instances, thesurvey requests that the first user provide an indication as to thedifference in quality of a first seat in a first venue area and a secondseat in a second venue area. In certain instances, the survey isprovided to the first user prior to tickets for the event being offeredfor sale. In certain instances, the survey is provided to the first userprior to tickets for the event being offered for auction. In certaininstances, the survey is provided to the first user during a sale ofevent tickets, wherein the sale is restricted to a subset of potentialticket purchasers. In certain instances, the survey is provided to thefirst user during a sale of event tickets, wherein the sale is open tothe general public. In certain instances, the survey is provided to thefirst user during a ticket selection or ticket purchase process. Incertain instances, the survey is provided to the first user at leastpartly in response to determining that the first user has purchased atleast one ticket. In certain instances, the survey is provided to thefirst user at least partly in response to determining that the firstuser has abandoned a first ticket-related request. In certain instances,the survey requests that the first user provide seat rankings for aplurality of seats and/or seating areas. In certain instances, thesurvey requests that the first user provide a ranking and/or a valuationfor a specified row. In certain instances, the survey requests that thefirst user provide a ranking and/or a valuation for a specified portionof a row. The above method optionally further comprises setting a ticketprice for at least one event ticket after tickets sales for the eventhave begun.

An example embodiment provides a system for enhancing yields, the systemcomprising code stored in computer readable memory configured to:provide a user interface via which a user can define and/or select eventcharacteristics with respect to a first event; provide a user interfacevia which a user can set an event filter used to locate one or morehistorical events that correspond to the filter; provide the user with apredicted sales rate for the first event.

With respect to the example system above, the event characteristics areoptionally selected from a group including at least one of thefollowing: primary performer; secondary performer; performance date; dayof week; time of day; venue. With respect to the example system above,the code is optionally further configured to receive a value countcorresponding to at least one characteristic. With respect to theexample system above, the code is optionally further configured toreceive a remapping indication from the user that maps a firstcharacteristic value to a second characteristic value.

An example embodiment provides a system for estimating demand, thesystem comprising code stored in computer readable memory configured to:store in computer readable memory a first demand estimate for a firstevent from a first source; store in computer readable memory a seconddemand estimate for the first event from a second source; access a firstweighting and a second weighting from computer readable memory; andcalculate a third demand estimate using the first demand estimate, thesecond demand estimate, the first weighting and the second weighting.

An example embodiment provides a system for providing demand estimatinginformation, the system comprising code stored in computer readablememory configured to: generate a graphical display including: demandrate data for a plurality of historical events; predicted demand ratedata for a future event, wherein in the predicted demand rate data is atleast partly based on at least a portion of the demand rate data for theplurality of historical events.

With respect to the example system above, the system optionally furthercomprises code stored in computer readable memory configured to: enablea user to select a subset of the graphically displayed demand rate datafor the plurality of historical events; and generate a graphical displayrelated to event characteristics associated with the selected subset.

An example embodiment provides a system for providing cash flowinformation, the system comprising code stored in computer readablememory configured to: display a plurality of rule inputs andcorresponding rule types; and providing pessimistic estimated cashflows, optimistic estimated cash flows, and/or estimated cash flows witha cash flow value between the pessimistic estimated cash flow and theoptimistic cash flow, corresponding to the plurality of rule inputs.

An example embodiment provides a system for selecting ticket prices toenhance revenues, the system comprising code stored in computer readablememory configured to: receive user cash flow enhancement instructions,wherein the user can select from a plurality of event demand scenarios,and the user can indicate which cost and income items are to be used ingenerating a cash flow enhancement process; and present to the user atleast a first ticket price, wherein the first ticket price was generatedat least in part by the cash flow enhancement process.

Embodiments described above can be implemented using computer codestored in computer readable memory. The computer code can be executed byone or more systems, such as ticketing and/or yield management systems.Aspects of two or more of the above embodiments can be combined.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will now be described withreference to the drawings summarized below. These drawings and theassociated description are provided to illustrate example embodiments ofthe invention, and not to limit the scope of the invention.

FIGS. 1A-B illustrate an example system embodiment that can be used inconjunction with processes described herein.

FIG. 2 illustrates a first example process.

FIG. 3 illustrates a second example process.

FIG. 4 illustrates an example estimated valuation characteristics for avariety of population groups.

FIG. 5 graphically illustrates example estimated valuation patterns fora variety of population groups.

FIG. 6 illustrates an example user interface that includes informationregarding declining prices.

FIG. 7 illustrates an example table illustrating how revenues areaffected using different pricing approaches.

FIGS. 8A-C illustrate example auction user interfaces.

FIG. 9 illustrates an example queue status user interface.

FIG. 10 illustrates an example notification request user interface.

FIG. 11 illustrates an example survey form.

FIG. 12 illustrates an example presale opt-in user interface.

FIG. 13 illustrates an example yield management system event demandforecast user interface.

FIG. 14 illustrates an example data import user interface.

FIGS. 15A-C illustrates example user interfaces for defining andselecting characteristics.

FIGS. 16A-B illustrate example user interfaces for defining acharacteristic value.

FIG. 17 illustrates an example user interface for defining relative areavalues.

FIG. 18 illustrates an example user interface for defining a filter.

FIG. 19 illustrates an example characteristic override user interface.

FIG. 20 illustrates an example event demand forecast user interface.

FIG. 21 illustrates an example user interface for displayingcharacteristic value clustering.

FIG. 22 illustrates an example user interface for setting up a cash flowanalysis.

FIG. 23 illustrates an example cash flow schedule user interface.

FIG. 24 illustrates an example cash flow enhancement user interface.

FIG. 25 illustrates an example user interface for displaying demandestimates.

FIG. 26 illustrates an example user interface for shifting seatingcapacity between price or minimum bid levels.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The present invention is related to resource allocation, and inparticular, to apparatus and processes for electronically allocatingfinite or limited resources.

Throughout the following description, the term “Web site” is used torefer to a user-accessible server site that implements the basic WorldWide Web standards for the coding and transmission of hypertextualdocuments. These standards currently include HTML (the Hypertext MarkupLanguage) and HTTP (the Hypertext Transfer Protocol). It should beunderstood that the term “site” is not intended to imply a singlegeographic location, as a Web or other network site can, for example,include multiple geographically-distributed computer systems that areappropriately linked together. Furthermore, while the followingdescription relates to an embodiment utilizing the Internet and relatedprotocols, other networks, such as networked interactive televisions,and other protocols may be used as well.

In addition, unless otherwise indicated, the functions described hereinmay be performed by software modules including executable code andinstructions running on one or more general-purpose computers. Thecomputers can include one or more central processing units (CPUs) thatexecute program code and process data, memory, including one or more ofvolatile memory, such as random access memory (RAM) for temporarilystoring data and data structures during program execution, non-volatilememory, such as a hard disc drive, optical drive, or FLASH drive, forstoring programs and data, including databases, which may be referred toas a “system database,” and a wired and/or wireless network interfacefor accessing an intranet and/or Internet. In addition, the computerscan include a display for displaying user interfaces, data, and thelike, and one or more user input devices, such as a keyboard, mouse,pointing device, microphone and/or the like, used to navigate, providecommands, enter information, provide search queries, and/or the like.However, the present invention can also be implemented using specialpurpose computers, terminals, state machines, and/or hardwiredelectronic circuits.

In addition, the example processes described herein do not necessarilyhave to be performed in the described sequence, and not all states haveto be reached or performed. Further, certain process states that areillustrated as being serially performed can be performed in parallel.

The processes and systems described below relate to systems andprocesses that enable system users to sell, license, or acquirespecified quantities of units (each, a “Unit”), which may be ininventory, using a dynamic pricing process. Each Unit may be unique oridentical to other Units that are being offered. Each Unit may becomprised of one or more elements.

One example of a “Unit” may be a single event ticket. Another example ofa “Unit” may be a group of event tickets. Another example of a “Unit”may be a group of event tickets and pieces of related merchandise.However, a Unit need not consist of or include one or more eventtickets, and may instead consist of another item or items, such asairline tickets.

In one example embodiment, user terminals access a ticketing computersystem via a network, such as the Internet, using a broadband networkinterface, dial-up modem, or the like. By way of example, a userterminal can be a personal computer, an interactive television, anetworkable programmable digital assistant, a computer networkablewireless phone, and the like. The user terminal can include a display,keyboard, mouse, trackball, electronic pen, microphone (which can acceptvoice commands), other user interfaces, printer, speakers, as well assemiconductor, magnetic, and/or optical storage devices.

An example user terminal includes a browser or other network accesssoftware capable of performing basic network or Internet functionality,such as rendering HTML or other formatting code and accepting userinput. The browser optionally stores small pieces of information, suchas digital cookies, in user terminal non-volatile memory. Theinformation can be accessed and included in future requests made via thebrowser. By way of example, a cookie can store customer, session, and/orbrowser identification information which can be accessed by a ticketapplication executing on a remote ticketing computer system.

The user can access information on Units that are available for purchaseor licensing, submit purchase requests for the Units, and purchaseUnits. Similarly, other users can submit Units to be sold via a userterminal browser or otherwise.

By way of example, a Web page or other user interface can be presentedto a user, listing some or all of the following: the event for whichUnits are being sold, an event venue, event ticket groups for whichtickets are being sold, the current ticket price for a specific seat orfor seats in one or more ticket groups, a quantity field, wherein theuser can enter the quantity of Units (e.g., tickets) that the user isbidding on, is interested in purchasing, offering to purchase, etc.

The ticketing computer system can include ticketing servers, accountmanager servers, a credit card authorization system, an internalnetwork, request routers, ticket request, data and status queues, and aninterface to the Internet. The ticketing computer system can host a Website, accessible by users, for selling Units, such as tickets or otherresources or inventory. The ticketing computer system can include one ormore databases whose data can be accessed as needed. For example, thedatabases can include a user account database, that stores user contactinformation, billing information, preferences, account status, and thelike, that can be accessed by other portions of the ticketing computersystem, such as by account manager servers. A survey database can alsobe provided that stores survey results from consumers related to, by wayof example, ticket pricing and/or seat or section ranking. The surveydatabase can also store survey results from consumers related to, by wayof example, ticket pricing and/or seat or section ranking. Optionally,the survey or other database stores some or all of the followingcharacteristics of the ticket buying population: population size ofrelevant ticket buying population, income, predicted or estimatedpatience, the ticket price that would cause an estimated percentage ofthe relevant population not to buy a ticket, and the ticket price thatwould cause an estimated percentage of the relevant population not buy aticket. Once or more of the databases can store information receivedfrom one or more users via various user interfaces, such as thosedisclosed herein.

The ticketing computer system can optionally include a front endswitching network, a state and information data cache which can bestored in a set of servers that forms a high capacity front end, a setof servers that includes application servers, a set of serversresponsible for controlling queues of transactional customers, and acore ticketing server system. For example there can be request queuesfor Internet users and/or for phone users.

Where the Units include tickets, one or more ticket databases accessibleby the ticketing computer system are provided that store ticketinformation records for tickets, including, for example, barcodeinformation, event name, event date, seat identifier, ticket holder nameor other identifier of a current ticket holder, names or otheridentifiers of past holders of the ticket, a ticket valid/invalidindicator, and an indicator that as to whether the ticket has been used.In addition, an event database can be provided that stores informationregarding events, including the venue, artist, date, time, and the like.Not all of the foregoing systems and components need to be included inthe ticketing system and other systems and architectures can be used aswell.

FIGS. 1A-B illustrates an example system embodiment that can be used inconjunction with the pricing and/or ticket sale processes describedherein. Not all of the illustrated systems and components need to beincluded in the system and other systems and architectures can be usedas well. With reference to FIGS. 1A-B, a user terminal 102 is coupled toan example Unit distribution system (e.g., a ticketing system) 104 overthe Internet 105 using HTTP/HTTPS. An example web proxy system 106includes an optional load balancer 108 and web proxy processors 110, andcan selectively block or route an inbound request from a user browserexecuting on the terminal 102 to an appropriate internal destination,which can be a queue or other destination server.

The illustrated example system 104 includes an example Web applicationsystem 112, which includes an optional load balancer 114 and Webapplication processors 116. A general transaction system 118 includes anoptional load balancer 120 and transaction processors 122 that are usedto generate transactional pages (such as some or all of the userinterfaces described herein), populate data caches, provide logic and/orrules for the transaction flows, provide users with queue relatedmessaging based on the queue position of a resource request, and tosequence requests. A cache cluster system 124 includes an optional loadbalancer 126 and processors 128. The cache cluster system 124 cachesdata and states for quick access by other system components.

An example processor system 130 is provided that includes an optionalload balancer 132 and Unit (e.g., ticket) sales processors 134.Optionally, the processors 134 can be used for a variety of types ofsales, including, by way of illustration and not limitation, auctions,fixed/static price ticket sales, and/or dynamic pricing of tickets. Theexample processors 134 conduct and/or manage the sales, keep track ofitems or sets of items being sold, the status of sales, and in the caseof auctions, the type of auction current bidders, the current bidamounts, the minimum bid increments, the current lowest bid pricesneeded to potentially win auctions, the number of items in a set beingauctioned off, and so on.

By way of example, the types of auctions can include one or more of thefollowing:

1. Pay what you bid auction, where an auction winner pays the amount thewinner bid

2. Uniform pricing auction, where auction winners for a given categoryof tickets/seats pay the lowest winning bid in the given category

3. A reverse auction, where the user specifies the quantity and qualityof seats for an event and how much the user is willing to pay forcorresponding tickets, and one or more Sellers can choose whether or notthey are willing to sell those tickets at those prices. For example, theSeller can be a ticket broker or other ticket purchaser.

The processors 134, or another system, can be used to host yieldmanagement software which forecasts event demand using historical data,cost data, and/or other data. In addition, the yield management softwarecan set initial ticket prices (or initial minimum bids in the case ofauctions), adjust ticket prices (or minimum bids), schedule ticket priceadjustments, and/or control the release of event seats.

The use of load balancers and multiple ticket sales processors canenable the sale/auction to continue, potentially with little or noperformance impact, even if a system component (e.g., a processor 134)fails. For example, if a sales processor fails, processes that wereperformed by the failed processor are optionally directed via the loadbalancer to another sales processor. A session cluster system 136includes an optional load balancer 138 and a plurality of processors 140and is used to manage sessions.

A member transaction repository database 142 stores user contactinformation, billing information, preferences, account status, and thelike, that can be accessed by other portions of the system 104. Inaddition, the database 142 can store an opt-in indication, wherein, withrespect to auctions, the user has agreed to have their bid automaticallyincreased by a certain amount and/or up to a maximum amount in order toattempt to ensure that they win a given auction. The database 142 canalso store a user opt-in for notifications regarding auctions, auctionstatus, and/or change in the user's bid status from losing to winningbid or from winning to losing.

The database 142 can also store a user opt-in for notificationsregarding non-auctions, such as for notifications when ticket priceshave been decreased in a price decay sale. Optionally, the database 142stores a user indication that a user will purchase a ticket for a givenevent for a given seating area (or areas) if the ticket price is at orbelow a specified amount, wherein if the ticket price meets the userprice criteria, the system automatically completes the purchase andcharges the user. By way of example, the user can specify such purchasecriteria via a web page hosted by the system 104.

Optionally, the database can store a user opt-in for notificationsregarding the release of additional seat tickets for an event. Forexample, a notification can be transmitted to the user each time seattickets are provided for sale or auction for a given event. Optionally,the opt-in can be limited to notifications for the release of seattickets in selected venue areas or ticket price bands.

An event database 144 stores information regarding events, including, byway of example, the venue, artist/team, date, time, and the like. Theevent database 144, or a separate database, includes ticket informationrecords, including one or more of barcode information, event name, eventdate, seat identifier, ticket holder name or other identifier of acurrent ticket holder, names or other identifiers of past holders of theticket, a ticket valid/invalid indicator, and/or an indicator that as towhether the ticket has been used. An event database server 146 is usedto provide event database access to other portions of the system.

An example database 148 is provided that stores one or more of Sellerticket sales rules. For example, with respect to auctions, the salesrules can include auction rules (e.g., criteria for what a winning bidactually pays, when the auction will start, when the auction will end),auction operator rules, bidder eligibility criteria, information on theUnits being auctioned, including a description, the reserve price (ifany) the minimum bid price (if any), the minimum bid increment (if any),the maximum bid increment (if any), the quantity available, the minimumquantity of Units that can be bid on by a given user, the maximumquantity of Units a given user can bid on (if any), the date the auctionends for the corresponding Units, the current bids, the current bidranking, corresponding bidder identifiers, bid ranking criteria, Unitcategories, and/or the like.

The database 148, or another database, can also store informationregarding non-auction Unit sales (e.g., ticket sales for an event), suchas a presale beginning date, where selected users (e.g., members of oneor more specified fan clubs, season ticket holders, holders of certaintypes of financial instruments, such as a credit card associated with aspecified brand or issuer, frequent ticket buyers, etc.) can purchasetickets at set prices before the general public can, a presale end date,an onsale beginning date, where the general public can purchase eventtickets at set prices, an onsale end date, the maximum quantity of Unitsa user can purchase, and so on. With respect to a non-auction sale wherethe price of certain Units are adjusted during a sales period (e.g.,wherein a ticket price is increased or decreased over the course of aticket sales period to enhance total revenues), the database 148 canfurther store some or all of the following: information regarding aminimum Unit sales price, a maximum Unit sales price, a minimum dollarincrement with respect to increasing a Unit sales price, a minimumdollar decrement with respect to decreasing a Unit sales price, amaximum dollar increment with respect to increasing a Unit sales price,a maximum dollar decrement with respect to decreasing a Unit salesprice, the data and/or time when price increments or decrements are totake place, and a limit on the number of Unit price changes within aspecified period (e.g., no more than one price change every four hours,no more than one price change every twenty four hours, etc.).

A survey and historical information database 149 can also be providedthat stores survey results from consumers related to, by way of example,ticket pricing and/or seat, area, or section ranking. In addition, thedatabase 149 optionally includes historical sales information (e.g.,rate of ticket sales per section or area for one or more historicalevents, total ticket sales per section for one or more historicalevents, rate of ticket sales per price band for one or more historicalevents, total ticket sales per price band for one or more historicalevents, etc.). The database 149 can also include actual/estimated costand revenue data.

A host network system 150 is provided that stores bids (e.g., winningand optionally losing bids), event, sales, and auction set-upinformation, section and seat information (e.g., quality or rankinginformation), seat to bidder allocations in the case of auctions, andcredit card processing.

By way of example, the ticketing system enables a user (the “Seller”) tosell (e.g., on behalf of himself or a principal) Units to multipleusers. Thus, for example, the Seller can optionally be acting as aticket issuer (such as an artist, sports team, event producer, orvenue), or an agent for the issuer, rather than as a reseller. Thus, theSeller can be a primary market ticket seller, rather than a potentiallyless reliable secondary market reseller. The Seller can also be areseller, such as a secondary market reseller where the seller hadpurchased the tickets from a source, such as a primary market ticketseller, and is reselling the purchased tickets to others.

By way of example, the ticketing system enables the Seller to sell Unitsto multiple users over a network at the same time or over a period oftime. The Seller optionally determines whether or not to impose eitheror both a Unit quantity maximum level and a Unit quantity minimum level,which can be stored in a system database, as previously discussed. Forexample, the Unit quantity maximum level can be set to a value that isless than the total quantity of Units being sold for an event. If both aUnit quantity maximum level and a minimum level are set, then a user canbe prevented from purchasing a quantity of Units offered by the Sellerthat is less than any Unit quantity minimum level or greater than anyUnit quantity maximum level.

Instead of a minimum or maximum level as just described, the Seller mayinstead offer users several specified choices when determining thequantity of Units to purchase. For example, the Seller may optionallyonly offer users the chance to purchase a Unit quantity that is amultiple of two, is an odd number, or is the number five, seven, eight,or other determined number. The foregoing bidding or purchaselimitations or requirements can be stored in computer readabledatabases, such as those previously described.

The ticketing system may condition a potential user's eligibility topurchase or bid for tickets, or specific ticket group for an event basedon certain user or other characteristics, which may include, withoutlimitation, whether the user has purchased or registered for a certaintype of membership, the user's past purchase history with respect otheritems sold or offered for sale by the Seller or a third party, where theuser lives (for example, bidders may be required to be within aparticular geographic region, within a particular governmental entity,such as a particular state or states, city or cities, zip code or zipcodes, or within a certain distance from a given location, such as avenue or the like), and/or whether the user meets certain financialqualifications.

As will be described below, the system described above (or othercomputer system) can use some or all of the following information to setprices dynamically (e.g., where the ticket price is varied based onchanging conditions) and/or statically (e.g., wherein a ticket price isset once based on certain parameters). For example, the price can be setbased on historical sales data, current sales data/patterns, actualand/or predicted costs, actual and/or predicted revenues,characteristics of the relevant ticket-buying population, venue seatingcharacteristics, the desirability of packing venue seats, minimum priceconstraints, maximum price constraints, and/or limits on the ability tovary prices more than a certain number of times in a specified period.In addition, ticket limits (e.g., a limit on the maximum number oftickets a user can buy) can optionally be set to increase the number oftransactions to sell a given number of seats, hence providing moreopportunities to collect data relevant to adjusting ticket prices and toadjust ticket pricing. Further, with respect to an auction, some or allof the foregoing information can to be used to set initial minimum bids,minimum bid increments, and maximum bid increments.

Thus, for example, optionally, a ticket price (for one or more tickets)can be dynamically set using a software model that determines a “MarketValue” based, in part, on maximizing or enhancing venue revenue based oninventory left to sell and on the pattern or rate of sales up to thecurrent time or other designated time (e.g., rate of ticket sales persection, total ticket sales per section to date, rate of ticket salesper price band, total ticket sales per price band to date, etc.).

For example, during a ticket sale a set of 2D sales curves can begenerated and stored, wherein the curves are associated with the venue,performer, etc. The x-axis of a curve corresponds to time (e.g.,relative to the beginning of the ticket sale). The y-axis corresponds tothe percentage of sellable inventory (e.g., seat tickets) sold for theevent (or, optionally, the percentage of sellable inventory stillunsold). Sellable inventory can correspond to the seat tickets allocatedto the ticket seller to sell, rather than all the seat tickets for theevent. In a future ticket sale, one or more of the previously generatedcurves can be retrieved and pattern matching is performed (e.g., tolocate a curve that is similar to the sales pattern of the currentticket sale). Based on the curve of the current sale, the system locatesa similar or matching curve from a past event sale, or the systemattempts to make (e.g., by adjusting ticket prices up or own) thecurrent sale curve match or be more similar to a sale curve from aprevious ticket sale. The phrase “match” is intended to include thosecurves who similarly meet predefined criteria, even if the matchingcurves are not identical.

By way of example, on detection of a match, or on detection of variancefrom a previous sales curve that the system is attempting to match, oneor more actions are taken or triggered that are expected to affectticket sales.

Examples of triggered actions include (but are not limited to) thefollowing actions which can affect short term and/or long term yield:

changing ticket prices up or down

recommending promotions to the seller

releasing additional seats

Optionally, sales curves for current and previous ticket sales can bemanually analyzed, at least in part, in order recommendations on futurestrategies to one or more Sellers.

Optionally, target or desired sales curves are generated and stored incomputer readable memory, and a dynamic pricing process, running duringa ticket sale, attempts to match that target curve by adjusting ticketprices, releasing seats, and/or using other techniques.

An example system executes pricing processes that take into account someor all of the following:

1) The demand for an event can be estimated or determined based on dailysale patterns compared to historical templates for similar events, orother selected events. For example, if an event at a given venueinvolves the performance of a given artist, the comparison can be maderelative to the last performance by the artist at that venue and/or at asimilarly sized venue in a similar market (e.g., in a venue in a city ofa similar size with a similar demographic), within a relatively recenttime period (e.g., the last 3 months, 6 months, year, or 2 years). Thus,for example, if an artist sold out at a venue in a first city within 24hours, the system can estimate that the artist will quickly sell out avenue in second city, where the artist is performing in the second citywithin 12 months (or other selected period) of the performance in thefirst city.

2) Based, at least in part, on these sale patterns, an estimate can bemade of the number of seats that will be sold up to the time of eventperformance at different ticket price levels for a given seat or venuearea (or in the case of an auction, at different minimum bid levelsand/or different minimum/maximum bid increments). For example, anestimate can be made that at a first, relatively low ticket price, avenue section is likely to be sold out. An estimate can be made that ata second, relatively higher ticket price, tickets for about 66% of seatsin a venue section are likely to be sold.

3) Often, the total value of tickets purchased to an event exceeds thatof ticket sales (e.g., the sum (face value of the tickets sold)). Forexample, ticket value (e.g., for a venue operator, artist, team, and/orother Seller) may include per capita income from concessions,merchandise, parking, etc., which will be referred to herein asancillary revenues. By way of illustration, tickets sold at a relativelyhigher price may also be expected to have relatively higher ancillaryrevenues associated therewith as the ticket purchaser may have arelatively higher income and relatively more disposable income. In orderto enhance or maximize total revenue (e.g., total ticket sales+totalancillary revenues), it may be desirable to set ticket prices low enoughto better ensure that a first desired amount of tickets are sold, eventhough the resulting dollar value of the ticket sales may not bemaximized.

By way of further example, if a given artist (or several artists thatperform in the same genre) has performed at several different venues,with correspondingly different ticket prices, within a certain period oftime (e.g., one year), if there are substantially different patterns ofsales, an inference can be made by the system that the different is atleast partly attributable to the different ticket prices. Thus, forexample, if a certain rate of sales or total sales is desired in orderto enhance or maximize total revenue, a ticket price can be set thatcorresponds to that of one or more previous events that had the desiredrate of sales or total sales.

In an example embodiment, a computer system, for example the ticketingsystem described above, receives, automatically (e.g., from a databasein response to a request or from another computer system) or manually(e.g., from operations personnel), information regarding costs and/orrevenue streams. For example, the cost information can include some orall of the following:

maintenance costs

employee/contractor costs (e.g., guards, maintenance crews, ushers,ticketing personnel)

energy costs (e.g., for electricity, natural gas, coal, etc.)

governmental fees (e.g., permit fees)

facility rental fees (if the facility is being rented for an event)

equipment rental fees

guaranteed revenues to performer or other entity

For example, the revenue streams can include some or all of thefollowing

ticket sales

concessions

merchandise

parking

advertising

Some of the costs and fees associated with an event may be known priorto the event, and some costs and fees may be estimated or predicted(with greater or lesser accuracy). For example, facility and/orequipment rental fees may be known prior to an event, while energy costsmay be predicted. By way of further example, advertising revenues may beknown prior to an event (e.g., because they may be fixed in on or morecontracts), while merchandising revenues may be predicted.

A cost can be a fixed cost, a cost can be associated with the venue orevent capacity, a cost can be assigned to a price level, and/or a costcan be related to the attendance. By way of illustration, with respectto janitorial costs, in some instances the cost may be a fixed cost,while in other instances the cost can be at least party based oncapacity or a price level. Some types of revenues have an associatedoffsetting cost and some revenues have little or no incremental cost(e.g., service fees in certain situations) or may have costs which arerelated to price and attendance.

The ticketing system can use the costs and/or revenues associated withan event to determine or predict/estimate the incremental value oftickets sold. Optionally, based on current, substantially real timeinformation on tickets sold, the daily pattern of sales as compared tohistorical norms (including sales information for one or more selectedprior events), and remaining capacity (e.g., unsold tickets for theevent, unsold tickets for the event and one or more other events, suchas where there are multiple performances by a given performed at a givenvenue), the system determines whether prices should be increased ordecreased at a given point in time or within a time period to enhance ormaximize revenue. For example, the minimum price or minimum bid amountof a ticket can optionally be set not to be less than the incrementalcost of having another attendee. Optionally, the minimum price of aticket can be set not to be less than the incremental cost of havinganother attendee plus the anticipated ancillary revenues associated withan additional attendee buying a ticket at that price level.

Optionally, the ticket price increase and/or decrease can be limited sothat the price will not increase or decrease more than a certain amount.The system can optionally vary the ticket prices without any timerestraints, or the system may be constrained via software as to howoften the ticket prices can be raised or lowered (e.g., once a day, oncea week, once every two weeks, etc.), and when the first price adjustmentwill take place. Optionally, the system may vary the ticket price moreoften as the event date approaches. For example, ticket prices may bereduced with greater frequency as the event date approaches if a certainamount or percentage of seat tickets remain unsold. By way ofillustration, the ticket inventory can be offered at a first priceinitially, until a certain number of days or time prior to the eventdate. If a specified percentage of the ticket inventory remains unsold(e.g., for the venue as a whole or for certain seat categories), thenprice adjustments will begin, wherein the closer to the event date, thelower the price of the tickets.

An example embodiment of managing event revenue yields will now bedescribed. For example, the yield management processes and techniquescan be used to enhance the yield from ticket sales and/or enhanceancillary revenues, for an event. However, different or modifiedtechniques and parameters can also be used.

Short-term Yield Management

The example processes described herein can be used for short-term yieldmanagement, such as by price and capacity adjustments during an eventsales period and/or slightly before a general sales period. For example,real-time adjustment of prices or allocation of seats can be performed.Price adjustment is optionally based, at least in part, on thecharacteristics of the ticket buying population (e.g., population sizeof relevant ticket buying population, income, predicted or estimatedpatience, the ticket price that would cause an estimated percentage ofthe relevant population not to buy a ticket, the ticket price that wouldcause an estimated percentage of the relevant population to buy a ticketor an additional ticket, etc.), and the venue configuration (e.g., thenumber and type of seating areas, and/or the number of price levels andthe capacity for each price level). Capacity can be dynamically shiftedbetween price levels to enhance revenues and/or profits.

A. Ticket-Buying Population

In this example, the ticket-buying population for a given event isassumed to consist of various groups or categories whose purchasingpatterns are based at least partly on disposable income and on theirpurpose for attending an event. Thus, the price tolerance of differentsubdistributions of potential ticket purchasers can be identified foruse in estimating demand at different pricing levels and to set ticketprices. Below are illustrative sample groups that might compose theaudience for a baseball event (or other event, such as a concert),although other groupings and grouping characterizations can be used aswell, such as for a baseball event or for other event types:

-   -   Showoffs: These individuals have the purpose of being seen at        the event, as opposed to enjoying the event itself. They are        willing to pay high premiums for the best seats and have no        desire for anything less than superior seating.    -   High income fans: These individuals are truly interested in the        event and have the means (e.g., are in the top 10%, 5%, 3%, 1%,        0.5%, or other selected percentage of the population with        respect to income) to purchase expensive seats. They are not        willing to pay ludicrously high prices and are willing to accept        seats of moderate quality.    -   Middle income fans: (e.g., with incomes between the high income        fans and the low income fans) These individuals are truly        interested in the event but are not capable of purchasing the        highest-priced tickets. They are willing to accept seats of fair        quality.    -   Low income fans: This group is similar to the middle income        group, but with a more limited capacity for purchasing        moderately-priced seats (e.g., are in the bottom 40%, 30%, 20%,        10%, 5%, 3%, 1%, 0.5%, or other selected percentage of the        population with respect to income).    -   Socially oriented: This group (sometimes referred to as “party        animals”) is interested in the social interaction related to the        event and have a lesser or no interest in the event itself. They        therefore want to purchase the least-expensive seats, away from        the general population.    -   Brokers: Members of this group are not interested in attending        the event but are attempting to derive income by reselling high        quality seats to individuals of middle income or above.

1. Population Size and Market Inefficiency

For the purposes of yield management, such as short-term yieldmanagement, optionally the size of the population (e.g., the populationof potentially or likely interested consumers in the event) is assumedto be substantially fixed and dependent on the nature (e.g., artist,venue, date, time, and/or other event characteristics) of the event. Thechanging of prices can affect demand as far as the number of tickets anindividual is willing to purchase. Other embodiments can take intoaccount that the population size may vary. Additional factors, such asthe time of year, the weather, such as temperature, rainfall, snowfall,humidity, (e.g., the weather in a period preceding a ticket sale orevent, current weather, and/or anticipated weather), the current and/oranticipated economic conditions (e.g., consumer confidence, whether theeconomy is in a recession, whether the economy is growing at a certainrate, trend on consumer spending on entertainment, etc.), and/or theoccurrence of natural or man-made disasters (e.g., an earthquake, alarge scale flood, a large scale fire, a hurricane, an act ofterrorism), can be taken into account when setting ticket prices.

2. Population Group Parameters

For the purposes of a modeling example, each of the populationsub-groups can be characterized by a set of parameters. The followingare example parameters, one or more of which can be used in the model:

U: the total size of the estimated interested population

p: the population proportion. This is defined as the number ofindividuals in the group divided by U.

λ: the patience parameter. This parameter describes the probability ofthe individual attempting another transaction if a transaction failsbecause either pricing is too high or capacity is temporarilyunavailable (which can be determined, for example, via surveys and/or byexamining historical ticket sales information). To enhance theefficiency of the determinations herein, it is assumed that the patienceparameter decays geometrically over time, although are decayprogressions can be used as appropriate. Thus, the parameter for asecond attempt is λ and the parameter for a third attempt is (λ·λ).Other decay formulas can be used as well.

α: the ticket increase parameter. It is assumed that most individuals inan applicable population will buy a predetermined number of tickets.They will either buy no tickets, if pricing is above a certain maximumvalue, or add tickets to their order if pricing is significantly belowthis maximum threshold. The parameter a describes the price as apercentage of the maximum threshold that would or is likely to stronglyencourage an individual to add a ticket to his order (although theindividual may still elect not to purchase such ticket). To enhance theefficiency of the determinations herein, it is assumed that a ticketwould be added to the order if the price dropped below (α·α) times thethreshold value, although this might not be the case in actual practice.Optionally, an assumption can be made that most users will either buy notickets or at least two tickets as many people do not want to attend anevent alone. Thus, a prediction or assumption that a third ticket wouldbe added to the order if the price dropped below (α·α) times thethreshold value.

3. Area Parameters

Each group may have a different set of purchasing parameters for eacharea of the venue.

t: The number of tickets in an order.

m: The predicted maximum price individuals are willing to pay fortickets in the area. In an example embodiment, the m parameter isdefined via the lognormal probability distribution.

$\begin{Bmatrix}{\frac{1}{\sqrt{2\pi}\sigma\; x}{\mathbb{e}}^{- {\frac{1}{2\sigma^{2}}{\lbrack{{\ln{(x)}} - \mu}\rbrack}}^{2}}} & {x>=0} \\0 & {x < 0}\end{Bmatrix}\quad$

The basis for this predicted distribution is as follows: While logicallypermissible, negative prices would rarely be practical, and such eventsare optionally not considered in this model, although other models canconsider the relationship between how much an individual is beingoffered to attend an event and the likelihood they would attend atdifferent offered amounts (such as may be done to help fill an eventvenue or as a reward or prize). In this example, an individual'ssubjective comparison of two prices is optionally assumed to be drivenmore by percentage difference than absolute difference. For example, $5added to a $600 ticket is relatively less meaningful (e.g., is lesslikely to affect a purchase decision) compared to $5 added to a $10ticket. However, other models can take into account that, for someindividuals, the absolute difference is as important, or is moreimportant than the percentage difference.

Model data is optionally accessed by retrieving from a database or otherdata store, or by a user manually entering the model data. The modeldata can include the median ticket price (or other statistical value,such as the mean ticket price) in the distribution, and the thresholdprice at which it is estimated that about 75% (or other specifiedpercentage) of given group or category of potential ticket purchaserswould be deterred from purchasing tickets. This estimate regarding thethreshold price which would deter potential purchasers can be derivedfrom surveys regarding such threshold price and/or from historicalresults for ticket sales at different price points.

An example estimated population distribution for a given event orevent-type is illustrated in FIG. 4. For a variety of potentialpurchaser types the following information is provided for different seatsections or categories of seats (e.g., Seat Category 1, Seat Category 2,Seat Category 3, Seat Category 4: an estimate as to the percentage thepurchaser type represents of the entire relevant potential purchaserpopulation; the estimated patience parameter; the estimated medianticket price the purchaser would be willing to pay; the estimatedthreshold price; and the estimated number of tickets that the averagepotential purchaser type will buy when the tickets are at thecorresponding median price. In this example, certain types of potentialpurchasers, such as “Show Offs” or ticket brokers are unlikely to beinterested in relatively poor/less expensive seats, such as those inSeat Categories 3 and 4, even if the ticket price was set to 0. Othertypes of potential purchasers, such as middle level, low income, andsocially oriented fans are likely to purchase more tickets as the pricedecreases. FIG. 5 illustrates graphically the number of potentialcustomers of a given potential purchaser type that is estimated would bya ticket for a given venue area at a given price.

B. Venues

1. Areas

Ticketed venues for non-general admission events can be considered to bedivided into areas. The boundaries defining these areas can bedetermined by physical separation of sections (e.g., aisles or venuelevels), by the innate desirability of seats (e.g., front row, rowbehind the stage, center row seats, obstructed view seats, etc.) whichare optionally determined via surveys, experience, and/or intuition, orvia other seat properties. From the perspective of short term yieldmanagement, these “separations” provide an opportunity to distinguishseats by value, collect demand information, and encourage customerpurchase decisions. Thus, for example, different prices can bedynamically set for different venue areas. Further, the frequency and/orpercentage or dollar amount in price change can be different for ticketsassociated with corresponding different venue areas.

2. Ticket limits

Venues or other Sellers can also impose ticket limits by area. Ticketlimits restrict the maximum number of tickets a customer (e.g., asidentified via a user identifier, password, mailing address, billingaddress, credit card number, bank account number, etc.) can purchase fora given event or across multiple events. These limits can optionally beimposed to better ensure fair access to quality seats, preventingcertain groups (e.g., ticket scalpers) from monopolizing prime seatingfor resale purposes.

Ticket limits can be incorporated into the yield management analysis.For example, smaller ticket limits increases the number of transactionsneeded to fill a venue area, creating more opportunity to adjust pricingand collect data. Thus, for example, a ticket limit can be set so as toincrease the opportunity to adjust pricing and collect data. Also,ticket limits can force customers to decrease a desired order size orchoose different venue areas to fulfill a larger order. Thus, forexample, different venue areas may be associated with different ticketlimits. By way of illustration, the ticket limit for a relatively moredesirable area (e.g., orchestra) may be set to 2 or 4, while the ticketlimit for a relatively less desirable area may be set to 8 or 12.Further, a purchaser may be allowed to purchase up to the ticket limitfor two or more areas. For example, a purchaser may be allowed topurchase up to 12 tickets, including up to 4 tickets in a first area andup to 8 tickets in a second area. In an example embodiment, a ticketlimit for a performer can include two or more performances. For example,the ticket limit can restrict a purchaser to a total of 8 tickets acrosstwo (or other number) of performances. Thus, the purchaser can purchase6 tickets for a first show by a performer, and 2 tickets for a secondshow by the performer, for a total of 8 tickets.

3. Minimum Pricing

An example dynamic pricing process takes venue (or other Seller) imposedminimum pricing (e.g., where a ticket for a venue area is not to be soldfor less than a specified amount) into account. A minimum price may beset for a variety of reasons, such as not to set a precedent of very lowticket prices, not to embarrass a performer, and/or to make sure theticket price (or a designated portion thereof) is sufficient to coverthe incremental costs associated with an additional event attendee.

By way of illustration, the economic model used by promoters sometimesguarantees certain revenue to the performer. If dynamic pricing were toset too low on a per seat price, the event may not generate sufficientrevenue to cover this guarantee. Often, there is a certain component ofa ticket price which is used to cover the cost of facility managementand operations, such as air conditioning and janitorial services. Ticketprices may optionally be set so as to be maintained at or above aminimum threshold so as to cover such costs.

4. Maximum Pricing

In a dynamic pricing model, a venue (or other Seller) may impose amaximum price for tickets in certain areas. Such limits may be motivatedby a desire for public perception of fair access (such that not only thevery wealthy can afford event tickets). Such maximum price constraintscan be used to limit or bound prices set by the dynamic pricing process.

5. Discount Pricing and Ticket Types

A Seller (e.g., a venue and/or event) can release discounted ticketsbased on customer type (e.g., children, seniors, students) or as aresult of special promotions. Optionally, these event tickets may beexcluded from dynamic pricing, although they can optionally bedynamically priced as well. Further, the size of the discount for agiven customer type is optionally dynamically varied based on some orall of the factors that are used to set price.

6. Contiguous Seat Problem

When assigning specific seats to ticket purchasers, the problem ofcontiguous seats becomes an issue. As an individual row fills, it ispossible that an order needs to be shifted to a different row withinferior seating, because insufficient contiguous seats are available inthe current row. The dynamic pricing process can optionally take thiseffect into account based on data collected over time.

For example, the contiguous seat problem presents an opportunity foranother form of optimization. Optionally, during normal sales, theticket system attempts to provide the consumers the best (e.g., highestranked, or highest ranked within a requested ticket group or venue area)available seats that fulfill the order (taking into account the quantityof sets requested by a given consumer) at a given instant of time. Thiscan leave single seats at the ends of rows or in areas where priororders have been abandoned. These single seats often take time to sell,and may not sell at all, as demand for lone seats is relatively low.Thus, even though there may be unsold seats in a given venue area, somecustomers interested in purchasing tickets for contiguous seats in thatarea cannot be satisfied. The short-term yield management processoptionally allows orders to be sorted so as to provide the possibilityof tighter packing of seats. This packing would provide a relativelyhigher yield.

For example, a lower ranked bid may be assigned tickets (or other Units)that are ranked higher than tickets assigned to a higher ranked bid ifsuch lower ranked bid is for a quantity of tickets that either or both(a) enables all (or most) tickets for seats in the same vicinity to besold while ensuring that tickets sold in a given transaction with apurchaser are for seats that are next to each other (unless thepurchaser requests that the seats not be contiguous or next to eachother) or (b) relatively increases or maximizes the total aggregatedollar value of ticket sales and/or ancillary revenues while ensuringthat tickets sold in a given transaction with a purchaser are for seatsthat are next to each other (unless the purchaser requests that theseats not be contiguous or next to each other).

Optionally, when attempting to reduce the number of single seats, lowerpriced tickets (e.g., tickets being sold for a relatively price orauction bid) will still be assigned seat tickets that are ranked lowerthan the seats assigned to tickets sold for relatively higher prices (orassigned to higher ranked bids in an auction), even if, as a result,tickets for some seats remain unsold.

7. Other Revenue Sources

As discussed above, in many instances, in addition to ticket revenue,there may be other sources of income for a venue. Often, concessions andmerchandise sales contribute significantly to final yield. Thus, certainexample short-term yield management algorithms and processes optionallyconsider occupied capacity as a component of yield. Thus, for example,the ticket price may be set to increase the number of tickets sold, evenif the ticket price(s) selected does not maximize revenues from ticketsales. Optionally, the ticket price(s) are set so that the expectedtotal revenue or total profit from the combined sale of tickets andancillaries (e.g., concessions, merchandise, parking, etc.) is enhancedor maximized.

By way of example and not limitation, setting and adjusting ticketprices can be treated as a linear programming problem, and linearprogramming, which is well known in the art, can be used tomathematically optimize or select the ticket price using the relatedvariables. A linear programming problem is generally an optimizationproblem in which an objective function (e.g., total revenue maximizationor total ticket sale maximization) and the constraints (pricingthresholds at which ticket sales and ancillary revenues drop, or dropmore than a specified amount) are linear. In an example embodiment,rather than just optimizing tickets sales or filing seats, yieldoptimization is performed to enhance or maximize total revenue to thevenue, the event/promoter, the performer and/or other entity from ticketsales and ancillary revenues, even if the ancillary revenues are theresult of sales from entities other than the ticket seller.

8. Venue Parameters

Example venue parameters can include some or all of the followingparameters:

-   c: the capacity of each area of the venue for which tickets are    being sold-   l: the ticket limit in each area-   i: the minimum allowed price in each area-   a: the maximum allowed price in each area-   o: the contiguous seat effect. A measured factor which when    multiplied by model generated yield provides a yield closer to what    would be observed in practice. For example, the measured factor can    be related to the average number of seats consumers are expected to    request for a given inventory. The number can be derived from sales    models and/or historical data taken from previous sales of the same    artist or venue and/or for similar artists (e.g., similar    demographic appeal and/or album/song/ticket sales).-   n_(f): Fixed cost or revenue per seat external to ticket price.-   n_(v): Variable cost or revenue per seat as a percentage of ticket    price.

By way of illustration, the price of tickets affects the average numberof seats consumers will ask for. The average number of seats consumersask for affects the packing efficiency of seats (e.g., how many orphanedseats are left where the system could not find a desired number of seatsin a row together for a ticket requester). In certain instances, thedesired yield of a section is when 100% of the seats are sold. Whenpacking efficiency goes down, the yield often goes down because seatsare left unsold. There is a measurable relationship between the ticketprice and the packing efficiency. As ticket pricing goes up or down, sodo the number of unsold seats. As a consequence, there will bethresholds at which a change in ticket price (upwards or downwards) willcause ticket revenues to decrease as a result of more seats remainingunsold. Thus, there is a balance between setting ticket prices andenhancing the number of tickets sold. Less expensive tickets may resultin lower overall ticket sales even though more tickets may be sold. Itmay be more profitable and the dollar value of ticket sales may behigher if ticket prices are raised and if buyers can only request or buyrelatively smaller blocks of tickets or are discouraged from buyinglarge blocks of tickets.

Ticket price is a deterministic predictor of the average number ofcontiguous seats consumers will ask for. Thus, if the ticket price isrelatively higher, buyers will tend to purchase smaller tickets forsmaller blocks of seats, making it easier to reduce the number oforphaned seats by packing the rows more efficiently, and hence revenuesare increased. If consumer buying patterns do not adequately match thepredicted buying patterns, at least partly based on real time salesdata, the ticket price can be dynamically adjusted (e.g., in real time)to obtain the desired yield and/or to reduce the average or median sizeof seat blocks being purchased by consumers.

C. Distribution Channels

Tickets sold through the example ticket system are optionallydistributed through one or more channels, such as: physical ticketoutlets, phones, box-office, the Internet, interactive televisions, andvoice response units. A given distribution channel may have anassociated a set of characteristics which may be significant from theperspective of yield management.

1. Abandonment Rates

A, σ_(A): Measures of the average and standard deviation of length oftime a customer is willing to wait in queue before deciding to foregopurchasing tickets, although other statistical measurements may be used.With appropriate messaging, this parameter can potentially be alteredfor a given channel. The abandonment rates can be estimated at leastpartly based on historical abandonment rates for different messaging.

For example, the queue abandonment rate can be influenced by controllingthe messaging or notification provided to the consumer in aticketing-related queue which estimates the associated wait time (e.g.,the time until the consumer's ticket request or ticket informationrequest is serviced). By way of example, when the request is serviced,the system can determine if there are seats available that correspond tothe user's request for tickets (e.g., the system can determine if thereare seats available that match the quantity and quality of requestedseat tickets).

By way of illustration, consumers may be frustrated, and more likely toabandon their place in the queue (e.g., by navigating to a different Webpage, by closing their browser, or otherwise), if the time estimateprovided in the notification is inaccurate and underestimates the queuewait time. For example, if the notification provides an estimate withone second resolution, it is possible during the wait in the queue, afirst notification may provide a 90 second wait time notification, and asecond, later notification may provide a higher wait time (96 seconds).This increase in time may result from factors that are causing the queueprocessing system to slow down (e.g., if a technical problem results ina temporary reduction in throughput capacity). If instead, thenotification provides a wait estimate rounded up or down to the nearestminute (or two, or three), it is less likely that the wait timedisplayed to the consumer with change, and hence consumers are likely tobe less frustrated and less likely to abandon their place in the queue.

Optionally, the wait time estimate can be in a “less than” format (e.g.,“the wait time is less than [specified time period]”), where thespecified time period is selected so that it is highly likely that theconsumer's request will be serviced before the time period expires. Forexample, the time period can be selected so that 90%, 95%, or at least99% of the time the consumer's request will be serviced before the timeperiod expires.

Further, if the consumer were not provided with a time estimate withrespect to expected wait period, in certain instances, such as when thequeue is of a medium length (e.g., 45-60 seconds) the abandonment ratemay increase. In certain other instances, such as when the queue is long(e.g., 3-4 minutes), the abandonment rate may decrease if no timeestimates were provided. In other instances, such as when the queue isshort (e.g., less than 10 seconds or less than 20 seconds), the presenceor absence of a time estimate may not significantly affect theabandonment rate.

Optionally, if a consumer's place in a ticket purchase queue (e.g.,where the consumer's place is far back in line, based on a determinationor estimate of the consumer's place or relative place in the queue)makes it unlikely that there will be an available ticket that meets theconsumer's ticket request once the consumer's request is serviced, theconsumer can be so informed. If available, the system can notify theconsumer regarding alternate performances for the event in which theconsumer is interested (e.g., additional performance times/dates by theperformer, such as for a concert, a play, a sporting event, or movie).Optionally, one or more links can be presented for such alternateperformances, which, when activated by the consumer, will cause a ticketselection and/or purchase process to be initiated (e.g., the consumerwill be provided an interface via which the consumer can select adesired ticket price category/section, the system will inform theconsumer if such tickets are available, and if so, the consumer canpurchase the tickets). Optionally, the consumer can also be informed viaa web page or otherwise of other events the consumer might be interestedin based on the initial event the consumer attempted to purchase ticketsfor and/or the consumer's past purchase history.

In an example embodiment, a prediction or inference is automaticallymade by the system regarding the consumer's interest in other eventsusing collaborative filtering. The predication can be based, in whole orin part, on the ticket purchases made by other consumers who have alsopurchased tickets for the performer/event associated with the initialevent. The recommendations can also be based on the ticket purchases ofother consumers whose interests (e.g., as indicated via an online surveyor by event ratings provided by the consumers) are similar (e.g., likethe same music if the event is a musical performance, like the same typeof plays) to those of the consumer of interest.

Thus, the notification selection (e.g., whether a time estimate isgiven, the resolution of the time estimate, the time estimate accuracy,whether the consumer is provided information indicating the likelihoodthat there will be suitable tickets available once the consumer's ticketrequest is serviced, etc.) can optionally be manually or automaticallyselected so as to reduce or increase the abandonment rate to enhance oroptimize tickets sales and/or total revenues for an event, for relatedevents (e.g., events with the same performer), or for the ticketing Website. The selection can be made prior to an event sale beginning, andthe selection can optionally be changed during a sale in response toreal time conditions (e.g., queue length). By way of example, it may beadvantageous to increase the abandonment rate for certain users whosequeued ticket requests are unlikely to be met (e.g., where it is likelythe event will be sold out by the time their request is serviced) so asto encourage such users to purchase tickets for a different event.

2. Transaction Rates

T, σ_(T): The average and standard deviation of the length of time istakes to complete a transaction for each distribution channel, althoughother statistical measurements may be used.

3. Simultaneous Transactions

P: The number of simultaneous transactions each distribution channel cansupport.

4. Seat Desertion Rate

S: The probability a customer will cancel a transaction (e.g., after thecustomer is presented with one or more available seats, such as thosepresented to the customer as best seats currently available) and willattempt another transaction hoping to get a better seat (“seat desertionrate”). Optionally, this parameter is defined for the Internet and notthe other channels as other channels tend to have relatively low seatdesertion rates. However, this parameter can be defined for one or moreother channels as well.

5. Transitional Seats

The combination of simultaneous transactions, transaction rate, and seatdesertion rate can be useful, although not required, in the yieldmanagement process. When the ticket system offers specific reservedseats for sale to a given customer, these seats are optionally held orreserved for a predetermined period of time (e.g., one minute, twominutes, three minutes, etc.) and are not available for other customersuntil the tickets are abandoned by the customer (e.g., the customerfailed to complete the purchase transaction within the predeterminedtime). This has the effect of reducing available inventory over a spanof time, such as several minutes, which can impact price adjustmentdecisions.

Thus, seats in transition include seats that have been requested and puton hold for a consumer but which have not been sold to that consumer.The existence of seats in the transitional state implies interest in thetickets at their current price. This is therefore a good measure orindication of demand and acceptance of a given set of prices.

Knowing that people are interested enough to request seats at that priceat least partly validates that the current price is not set to high andmay indicate room for upward adjustment of the ticket price. Optionally,the system can increase ticket prices in relatively small percentageincrements (e.g., 2%, 5%, 10%) until the ratio of transitional seats vs.total sellable inventory drops by an undesirable amount.

In addition, an abandonment rate can be tracked with respect to how manypeople who requested seats be put in the transitional state decided notto buy tickets for those seats. If the abandonment rate of transitionalseats is higher than a certain or specified threshold, this can providea reliable indication that the ticket price may be set too high and theabandonment rate can be used to make decisions on price reductions.

6. Sale Cost

C_(S): The cost (if any) per second of time (or other time period) acustomer is using a channel. For example, certain channels such as thephones have a cost associated with holding a customer in queue.

C_(T): The cost per ticket sold through a channel.

7. Rolls

A promoter or other entity may decide to add an extra event to aperformer's appearance in the same area (e.g., the same city, town,within a certain distance of the original venue, etc.) in the same ordifferent venue. The decision to add an extra event may be based ondemand shown for a prior event. Optionally, the optimization process canset ticket prices so as not to substantially reduce demand, and toprovide accurate information so the roll decisions can be intelligentlymade.

8. Visibility of Venue Data

In order for customers to make informed purchase decisions a customermay be provided with information as to the quality of seat in each area.For example, each seat or section may be provided with a rankingdisplayed to the user (e.g., via a Web page or otherwise). The rankingmay be based on surveys (e.g., ratings) of potential purchasers and/oron objective facts (e.g., the distance of a seat from a stage orperformance area, whether the seat is directly in front of a stage orperformance area or is off to one side, whether a seat has obstructed orunobstructed sightlines to the performance area, etc.). The ranking maybe provided in the form of a number ranking, a star ranking, a textranking, a color-coded ranking, or other ranking representation.Optionally, a user can request that seats having a certain ranking(e.g., seats having a specified ranking, a specified ranking or better,a specified ranking or worse, etc.) be highlighted in a display (e.g.,shown in a specific color code, shown in bold, shown blinking, etc.).Optionally, different users may be provided the same or a differentlevel of visibility with regard to this information. For example,optionally users having logged into a system account may be providedwith seat rankings, while other users are not provided with seatrankings.

9. Visibility of Consumer Demand

Each channel may have different attributes with respect to gatheringcustomer demand information. Some channels, such as the Internet, arehighly interactive making it easier for the customer to enter ticketpreferences thoroughly. Other channels, such as the voice response unit,optionally gather only a portion of the customer's preference on anygiven transaction so as not to unduly burden the customer and/or thevoice response unit.

Example relative ranking of distribution channels on a scale of 1 to 10,with 1 being the worst ranking or the lowest rate (these rankings maychange over time or be different for different types of events) areillustrated in the table below.

Ticket Phone Box Voice Measure Outlets Sales Office Internet ResponseAbandonment rate 1 6 1 10 6 Transaction rate 1 10 2 7 9 Simultaneous 5 310 1 3 transactions Seat desertion rate 1 1 1 10 1 CS Sale cost 2 10 1 110 CT Transaction cost 6 10 1 3 10 Venue visibility 7 6 4 1 10Visibility of customer 8 6 3 1 10 demand

The distribution channel ranking is optionally used as a weightingfactor. For example, the weighting factor can be used to weight ormodify other variables in price setting equations and process. By way ofillustration, the online abandonment rate can be weighted, based atleast in part on the Internet channel ranking, more heavily than thephone abandonment rate.

Optionally, the ranking can be expressed as a percentage or fractionalfactor that is multiplied against that channel's contribution to thevalue of a variable in a price setting or other equation. For example,the rankings can be on a scale of 0% to 100% or 0.1 to 1.0

By way of illustration, assume phones are given a ranking correspondingto 0.2, and the phone distribution channel has a transaction rate of 10per min. If the transaction rate across all channels were totaled, theweighted contribution by the phone distribution channel would be 2 perminute (0.2×10). Thus, for example the total transaction rate can becalculated using the following formula:Total transaction rate=sum(r ₁TicketOutlets+r ₂PhoneSales+r ₃BoxOffice+r₄Internet+r ₅VoiceResponse)

where:

r₁ is the ranking for the Ticket Outlets Distribution Channel

r₂ is the ranking for the Phone Sales Distribution Channel

r₃ is the ranking for the Box Office Distribution Channel

r₄ is the ranking for the Internet (online) Distribution Channel

r₅ is the ranking for the Voice Response Distribution Channel

For example, the sales price increase can be related to the weightedtotal transaction rate or the weighted average transaction rate.

II. Pricing Algorithms

One or more pricing algorithms or processes can be used. Such as, by wayof example:

Static pricing assigned based on historical data

Pre-sale algorithms that sort customer bids for an early phase of ticketsales

Time-of-sale algorithms that adjust ticket prices in real time

Pre-Sale (Scale the House)

With respect to ticket auctions, in an example embodiment, a ticketpre-sale scales the house based on customer bids for tickets. In thiscategory there can be one or more subcategories, each optionally havingthe same variants. For example there can be three subcategories, eachhaving three variants, forming a nine-by-nine matrix, although fewer,additional, or different subcategories and variants can be used.

Subcategories:

One-shot scale-the-house

Olympic scale-the-house

Mercenary Olympic scale-the-house

Variants:

Flat

Stratified

Continuous

1. One-Shot Scale-the-House

Under this approach, a customer makes a single bid indicating an areawithin the venue, a number of tickets, and a price per ticket. Forexample: Area 1, 2 tickets, $100 per ticket.

The algorithm sorts the relevant bids from highest to lowest and assignsseats in order, highest bid first, until an area is filled or all bidsare accepted.

2. Olympic Scale-the-House

Under this approach, named for the way ticket for the Olympic Games aresold, a customer makes bids, including price per ticket and number oftickets, for each area or for a subset of the areas within the venue.For example: Area 1, $100 per ticket, 3 tickets; Area 2/, $50 perticket, 4 tickets; Area 3, $0 per ticket, 0 tickets.

Bids for seats in the best area are sorted from highest to lowest andassigned in order, highest bid first, until the area is filled or allbids for that area are accepted. Customers' assigned seats in that areaare removed from the pool of bids, and the process is repeated for thenext area. The process iterates until seats in the last area areassigned.

3. Mercenary Olympic Scale-the-House

Under this approach, bids are accepted as for Olympic Scale-the-House.However, the algorithm assigns seats based not on highest bid but on thehighest premium that each bidder is willing to pay. By way of example:

Each customer's bids for each area are compared to the average bid forthat area. The difference is considered the premium the customer iswilling to pay. For example:

Customer Average A Customer A Customer B Customer B Bid Bid Premium BidPremium Area 1 $250 $300 $50 $250 $0 Area 2 $50 $200 $150 $40 −$10

In this example, Customer A bids more than Customer B bids for seats inArea 1; however, because seats are assigned based on premiums, CustomerA might get seats in Area 2 and Customer B in Area 1.

4. Variants

Example variants for each pre-sale subcategory include:

Flat: The price that all customers in an area pay is the lowest bid forthe area.

Stratified: Each area is subdivided into p redetermined strata. Theprice that all customers in a stratum pay is the lowest bid for thatstratum.

Continuous: A customer pays what the customer bid.

With reference to FIG. 7, which illustrates the estimated revenue effectfor different subcategories and variances, the Continuous variant of theMercenary Olympic Scale-the-House approach may maximize revenue relativeto the other approaches, although if users find it unfair for a givenevent, event-type, or all events, other approaches may be used. Thus,for example, in certain instances, the Flat variant of the One-shotapproach may be selected.

B. Time-of-Sale

A time-of-sale approach adjusts prices in real time. In this categoryare three subcategories:

Price decay

Simultaneous boundary sellout

Market maker

1. Price Decay

With this approach, tickets are priced high and sold. At predeterminedintervals, prices drop and sales continue, until a minimum price isreached or tickets sell out.

2. Simultaneous Boundary Sellout

This approach utilizes information about the number of potential buyersenqueued for an opportunity to buy tickets on the Web. Prices for allareas of the venue are rapidly adjusted to ensure the following outcome:All (or selected) areas sell out at the point where all customers havetried to buy tickets.

Optionally, a given ticket price can be based on an estimated ticketmarket value. For example, the ticket price can be determined based onconsumers purchasing at a particular price with the understanding thatconsumers have knowledge of future pricing and the current bestavailable seats (e.g., the best available seats in the venue as a wholeor in a venue area specified by the potential buyer). The systemoptionally operates on one or more of the following understandings:

1) The standard face value of tickets is typically below free marketprice for high demand events.

2) Consumers have a preconceived notion of the approximate “true” orperceived value of tickets, and in general will not pay prices abovethis value.

3) In a dynamic environment, consumers have an incentive to pay neartheir preconceived value for seats that they are interested in as earlyas possible in order to purchase tickets in prime or preferred locationsahead of other consumers.

By way of illustration, an example embodiment can place tickets for anevent or sections (e.g., including one or more seats, rows of seats,other groupings of seats) of an event up for sale at a particular pricefor a certain period of time. For example, a consumer can access a Website hosted by the ticket system. The consumer can search for or requestinformation regarding an event. The system can cause a user interface tobe presented on one or more consumer terminals (e.g. via a Web pagetransmitted via the ticket system to a browser), via which theconsumer(s) are informed of one or more of the current ticket price(e.g. for a given section or category of seat; or, for a generaladmission event, the current general admission ticket price), the timeat which ticket prices will be lowered, and/or the current bestavailable seats overall and/or within a specified section or sections.

By way of further example, the user interface can include the timing ofonly the next price reduction (e.g. “the next price reduction to $200will take place on Apr. 1, 2005), or provide a schedule for pricedeductions (e.g. “the next price reduction to $200 will take place onApr. 1, 2005, the following price reduction to $150 will take place onApril 15, and the last price reduction to $90 will take place on April30”). Consumers can then chose to purchase at the current price with theopportunity to acquire higher quality seats or wait until the pricefalls knowing that available seats at that time are likely to be oflower quality. The process is optionally repeated until the event (or aselected set of event seats) is sold-out or optionally, until the pricehas reached a predetermined minimum price (e.g., face value) threshold.

FIG. 6 illustrates an example user interface. The example user interfacelists the event name, the event date, the current best availableseat(s), the current corresponding seat price, the timing of the nextprice reduction, the limit on the quantity of tickets a user canpurchase for the event.

By way of example, the timing, rate and/or amount of price reduction canbe based on one or more of the following:

1. how many sellable seats are in the venue

2. ticket sale history (e.g., ticket prices, ticket sale rate, etc.) forcomparable events (e.g. events having the same artist as the currentevent)

3. the remaining time until the event

4. the rate of ticket sales for the event

5. the current and/or prior ticket requests

Thus, an example pricing process enables a pre-selected number of seatsto be sold starting at a pre-defined premium or relatively higher pricedetermined by the venue, event artist/performer, and/or ticket systemoperator. Ticket prices can be reduced on a sliding scale in incrementsover an allotted time frame, thereby giving consumers the option topurchase tickets immediately or risk the wait, wherein the ticket pricewill be less, but the available tickets may be of lower quality. Theticket availability and pricing information presented to the consumer isprovided in substantially real time, so that consumers will know what iscurrently available. In addition, variables within this process canoptionally be adjusted in substantially real-time with the changes beingreflected substantially immediately. The ticket seller/ticket systemoperator can optionally charge a percentage base or flat fee, by way ofexample, for ticket sales.

Optionally, during a sale or auction, if there is a single (rather thantwo or more) unsold seat at the end of a row, or a single unsold seatwith sold seats on either side, such single seats may have their pricesdecreased further and/faster than the price reductions for seats wherethere are two or more unsold contiguous seats.

Optionally, a ticket price (for one or more tickets) can be dynamicallyset using a software model that determines a “Market Value” based, inpart, on feedback from consumers and facility staff regarding therelative value of seats within a venue.

FIG. 17 illustrates an example user interface used to obtain anestimated value of seats in various price levels compared to the seatsin price level 1. The user interface includes a relative price guessmatrix, wherein the size of the matrix may correspond to the number ofprice levels or seating areas. In the illustrated example, the matrixsize is 5 by 5 corresponding to 5 price levels. The user enters numbersin the matrix (the “Guessed relative price level values”). These numbersare guesses or estimates of how good seats in a specific price level arerelative to the seats in another price level. Based on the providedrelative values, the program calculates an estimated value for seats ineach price level relative to the seats in price level 1 which areautomatically inserted into the estimated relative values matrix.

The elements of the illustrated matrix are numbers representing arelationship between different price levels. The elements representingrelationships between the same price level (elements in positions (1,1),(2,2), (3,3), (4,4) and (5,5), in this example) will have a value ofone. In this example, when a user enters a value in cell (1,4)representing the value of price level one relative to price level 4,using that value, n, the program assigns a number to the cell (4,1),value of price level 4 compared to price level 1 which is the reciprocalof the value entered in the cell (1,4), 1/n.

With respect to the following scenario, the information entered in thematrix so far indicates: Price Level (PL) 1 is twice as good as PL 2,three times better then PL 3, four times better than PL 4, and 5 timesbetter than PL 5. The second column indicates that PL 2 is twice as goodas PL3, which is not completely consistent with the first column'svalues. According to the first column, PL 2 is one and a half timesbetter than PL 3. The system can automatically adjust the relativevalues so that they correspond (e.g., by using an average value, ageometric mean, a logistic regression, (take log of ratios, then performregression and exponentiate the results), or other value) or provides adiscrepancy notification to the user so that the user can adjust one ormore values. For example, the ratio of PL3 to PL2 can be automaticallyadjusted from 0.5 to 0.46. The adjusted ratio values are automaticallyinserted into the estimated relative values matrix.

PL1 PL2 PL3 PL4 PL5 PL1 PL2 2 PL3 3 2 PL4 4 PL5 5

Instead of, or in addition to using the matrix cells, optionally a usercan enter estimates using the fields in the text “I consider the valueof seats in price level ______ to be ______ times better than those inprice level ______”, towards the bottom of the user interface.Advantageously, this is particularly helpful for users that find usingthe matrix to enter guesses conceptually difficult as the text thataccompanies the fields clearly state what the numbers being enteredrepresent. Once the user enters the desired numbers, the user can clickthe “add guess” control, and the user defined value is automaticallyinserted into the appropriate matrix field.

Clicking the “Initialize Guesses” control button initializes the cellsto a default value, such as zero. Clicking the “Transfer from Prices toGuesses” control causes the current seat ticket prices to be convertedinto ratios (e.g., (current seat ticket price for PL1)/(current seatticket price for PL1)) and then displayed in association with the userrelative price guesses so the user can compare the two ratios, andadjust the user guesses and/or ticket prices as appropriate. Clickingthe Transfer from Estimates to Guesses button takes the estimatesgenerated by the system (e.g., the adjusted rations as discussed above)and substitute them into the Guesses matrix.

It is often difficult to establish the “fair market” price of seats inadvance of a sale. For example, the fair market value for a ticket to abaseball game may vary on the current performance of one or both teamsin the game. Similarly, the fair market price of seats to a concert maysuddenly increase based on a television appearance by the performed, ora record release. Further, facility staff members often have difficultysetting appropriate prices breaks within a venue so as to maximize oradequately enhance revenues. However, in order to better price seattickets, consumers can provide feedback regarding the relative value ofseats based on location, where the feedback can be used to at leastpartly set ticket prices.

In an example embodiment, a ticket pricing process uses feedback from apre-sale with or without dynamic pricing to establish the generalpricing for use during a sale to the general public. For example, asample embodiment might display (e.g. via a Web page transmitted fromthe ticket system to a consumer computer web browser) a survey toconsumers requesting input regarding the relative value of seats invarious areas of a venue.

By way of illustration, consumers are optionally asked to provide therelative value for first row seats vs. third row seats, or side seats ascompared to center seats. The relative value can be specified one ormore ways. For example, consumers may be asked to rank one row (orsection) as compared to another row (or section), or the user may asked,that if tickets for a certain row (or section) have a first price, howmuch more would the consumers pay for a different, better row (orsection). Consumers can also be asked how much they would pay for acertain row (or section) and how much consumers would pay for aspecified better row (or section). The survey results can beelectronically received by the ticket system, and the results can bestored in a survey database, and later retrieved for analysis and ticketprice and/or minimum bid setting. Survey questions can optionally bevaried, so that certain consumers receive different survey questionsthat certain other consumers. The survey questions can be transmitted tousers prior to the pre-sale, during the pre-sale, prior to the sale tothe general public, during the sale to the general public, and after allticket sales for the event have concluded.

After an accumulation of such relative value statistics based onconsumer feedback, a software model for the relative pricing structureof a venue and/or event is established. Using one or more dynamicpricing methods, a subset, which is optionally a relatively small subset(e.g., 10% or 20% of seats) so as to provide the opportunity to gainticket sales information and better determine the value of tickets notyet sold, of a venue's available inventory is put on sale to consumersbelonging to all or a selected subset of the population. Thus, thesurveys can be used to set initial ticket prices and initial minimumbids, and to dynamically set ticket prices during an event ticket saleand minimum bids in an auction.

In an auction, the “average” ticket price can be calculated one or moreways. The average ticket price can be used to set future minimum bids.By way of example and not limitation, one or more of the followingcalculations can be used:

-   -   pure average (total per seat dollar value of all bids/number of        bidders/seats);    -   pure average but throw away top and bottom x % (to avoid a        misleading skew) and/or throw away unreliable or untrustworthy        sources;    -   utilizing the standard deviation or curve of original bid        values;    -   breaking up seats into quality based blocks and performing the        average calculation within each block rather than across all        seats in the auction.

Optionally, the tickets are auctioned of to the subset population.Optionally, auction winners are selected by allocating seats accordingto the bid amount (e.g., the highest bid is assigned to a best seat, anext highest bid is assigned to a next best seat, etc., until there areno more seats left). Optionally, the auction winners can pay what theybid, or in another embodiment, the price the auction winners pay is notwhat they bid but rather a calculated “average” (or other statisticalmeasurement) across their bid values. Thus, winning bidders within agiven section or block of seats (or, optionally the venue as a whole)will pay the same price, even if they bid different amounts. In additionto the ticket price for purchased tickets, winning bidders areoptionally charged delivery, service, and/or other fees.

The selected subset members optionally have a common characteristic, forexample fan club members or consumers that purchase a certain number(e.g. 5-10) tickets a year. The average or median (or other statisticalmeasurement) price paid for seats can then be used as a fixed pointaround which (e.g., within a certain range, wherein the range can be 0,or can extend above and/or below the average) the remaining inventorycan be priced to other consumers.

Optionally, a ticket price (for one or more tickets) can be dynamicallyset using a software model that determines a “Market Value” based onconsumer offers to receive presale priority. An example embodiment ofthe system operates on one or more of the following principles:

1) The standard face value of tickets (e.g., set by the venue and/or theartist) is below free market price for some or all of the availableseats for high demand events. For example, the face value of a front rowticket for a very popular artist is often far below the market value,and hence such a ticket is often resold for many times its face value.

2) In the ticket sales environment, consumers have an incentive to paynear “market value” for seats as early as possible in order to purchasetickets in prime or preferred locations ahead of other consumers.

3) income for an event can be enhanced by allowing customers, whoattribute the highest or relatively higher value for seats, to be givenpriority access to the event inventory.

By way example, a sample embodiment optionally accepts offers from thepublic for prime venue seats prior to the actual public sale period.Associated with a valid offer is a count of tickets requested. Thepresale price for the event is chosen so as to enhance or maximize therevenue for seats sold assuming that if the presale price is below acertain offer, the associated consumer will likely purchase seats.

Optionally, consumers offering a premium, such as offering to pay acertain predetermined percentage or greater of the chosen pre-sale price(or, optionally a certain dollar value) will be given passwords or otheraccess device (e.g., a link) allowing access to the inventory before thegeneral public. Optionally, this premium will be charged to the offeringconsumer whether or not the consumer later elects to buy a ticket duringthe presale discussed below. Optionally, instead, the premium may berefunded or not charged if the consumer does not elect to buy a ticket.Optionally, different fee amounts are charged for the ability toparticipate in corresponding different presale events (e.g., a presalefor a fixed price sale or participation in a presale auction). Forexample, a consumer may be charged a relatively higher fee toparticipate in the first day of a presale, and a relatively lower fee toparticipate in days 2-7 of the presale. Optionally, the systemdetermines how many seats are to be made available during the presalebased at least in part on the number of users that paid a presaleparticipation fee or other premium.

Consumers using these passwords (or other access device) can thenoptionally compete amongst themselves for inventory as would happenduring a public sale. For example, the tickets may optionally be soldbased in part on a first come first served basis. Via a purchase requestWeb page transmitted by the ticket system, an eligible consumer requestsseats at the presale price. The consumer is provided with the bestavailable seats meeting the consumer's criteria, and is given theopportunity to accept (e.g. purchase) or reject those seats. Aftercompletion of the presale (e.g., after a predetermined presale period),the remaining event inventory, or a selected portion thereof, is placedon sale for the general public.

Several example processes will now be discussed with reference to thefigures. FIG. 2 illustrates an example process that sets Unit (e.g.,ticket) prices. Prior to an event ticket sale beginning, initial ticketprices are set. In this example, at state 202, data that will be usedfor setting initial ticket prices is read from a database or isotherwise received from a data source by a computer system, such as thesystem 104 discussed above. For example, historical data and ticketsales set-up data can be used to set ticket prices. In this example,some or all of the following data is accessed:

Historical sales data (e.g., rate of ticket sales per price band/seatingarea for one or more historical events, total ticket sales per priceband/seating area for one or more historical events, ancillary revenuesfor one or more historical events, ratio of ancillary revenues totickets sold for one or more historical events, what ticket brokers havesold tickets for similar events);

Venue capacity (e.g., total seating capacity for event for which ticketsare being priced);

Venue seat configuration (e.g., the number and types of seating areas,the capacity of different seating areas, seat rankings, seating arearankings, etc.);

Cost data;

Price constraints (e.g., maximum ticket price for each seating areaand/or the venue as a whole, minimum ticket price for each seating areaand/or the venue as a whole);

Ticket discounts being offered to certain groups or types of potentialpurchasers;

Discounts being offered to certain groups or types of potentialpurchasers for ancillary items (concessions, merchandise, parking);

Limits on price adjustment frequency (e.g., once every 24 hours, twiceevery seven days, three times a month, etc.);

Limits on the total number of price adjustments per event (e.g., threetimes, four times, etc.);

Limits on the price decrease per price adjustment (e.g., $5, $7, or apercentage of the initial ticket price);

Limits on the total price decrease (e.g., $15, $21, or a percentage ofthe initial ticket price);

Limits on the price increase per price adjustment (e.g., $4, $6, or apercentage of the initial ticket price);

Limits on the total price increase (e.g., $12, $18, or a percentage ofthe initial ticket price);

Limits on the maximum number of tickets a user can purchase per event orfor multiple related events;

Limits on the minimum number of tickets a user can purchase;

Limits on the multiple of tickets a user can purchase (e.g., multiplesof two);

Characteristics of potential ticket purchaser population (e.g.,population size of relevant ticket buying population, motivation forattending event, income, predicted or estimated patience, the ticketprice that would cause an estimated percentage of the relevantpopulation not to buy a ticket, and the ticket price that would cause anestimated percentage of the relevant population to buy a ticket);

Limits on the minimum and/or maximum number of seats and/or specifiedvenue areas for which tickets are to be offered in the initial ticketoffering (e.g., a Seller may specify that at least half the seats and nomore than 66% of the seats are to be offered during the initial ticketsale);

Designation of which seats are to be offered during the initial ticketsale.

Some or all of the above sales setup instructions (e.g., the limitsreferenced above, the seats that are to be offered during the initialticket sale) may have been set by a Seller, performer, and/or venueoperator. Not all of the above data needs to be specified or used insetting ticket prices.

At state 204, some or all of the data accessed at state 202 is entered(e.g., automatically by the computer system or manually by an operator)into one or more pricing models/processes (e.g., softwaremodels/processes), such as those discussed herein, which utilize some orall of the data to set initial ticket prices. If the tickets are for ageneral admission event (e.g., where ticket holders are not assigned areserved seat), a single initial price may be set for all tickets beinginitially sold. If the event includes reserved seating, then different(or the same) initial ticket prices may be set for seats in differentvenue areas or price categories/bands.

At state 205, the tickets for seats designated to be released during theinitial ticket sale are offered for sale (e.g., via online Website,physical ticket outlets, phones, kiosks, box-office, etc.) to the publicor a select subset thereof.

At state 206, real time ticket sales data is monitored. The real timedata can include some or all of the following: the number of purchasersin one or more queues (e.g., software request queues for users in anonline ticket selection or purchase process, queues for phone usersattempting to select or purchase tickets, etc.); a current or recentrate of ticket sales for the event an/or related events (e.g.,optionally, broken down into rate of sales per venue seating area orprice band); total ticket sales to date for event and/or related events(e.g., optionally, broken down into total tickets sales per venueseating area or price band); remaining capacity for the event (e.g., forthe venue as a whole and/or for specific seating areas); demographicinformation regarding purchasers of event tickets; and demographicinformation regarding users that began, but abandoned a ticketselection/purchase process.

For example, some or all of the demographic information (e.g., age,gender, household income, ticket purchase frequency, event preferences,etc.) can be retrieved from a user's account record using a useridentifier (e.g., a password, userID, token, cookie data, etc.) providedby the user (e.g., manually or automatically by the user's terminal)before or during the ticket selection or purchase processes. Inaddition, the user's Web page navigation information (accessed via acookie or otherwise) can also be accessed and monitored so as determinethe level of users' interest in an event (e.g., how many times a uservisited a Web site or URL associated with the event or the eventperformer), which information can also be used to set and adjust prices.In addition, if the ticket sale allows users to specify the ticket priceat which they would purchase a specified quantity of tickets, then suchinformation can also be accessed.

Optionally, users accessing a ticketing website (e.g., to research orpurchase an event ticket) may be surveyed via an online survey or viaphone that asks the user at what price the user would purchase a ticketor a number of tickets for a given event or event type. The user canalso be surveyed as to as how the ticket price would affect the quantityof tickets the user is willing to purchase (e.g., if the ticket pricewere $25 dollars/ticket the user would be willing to buy four tickets,and if the ticket price were $35 dollars/ticket the user would bewilling to buy two tickets). Optionally, the survey questions areprovided only to selected users, such as users that have entered into aseat selection process for a selected event, users that have actuallypurchased tickets for a selected event, or users that began, butabandoned a seat selection and/or ticket purchase process.

At state 208, some or all of the data collected at state 206 is comparedto corresponding historical data, such as that accessed at state 202. Atstate 210, based on some or all of the data obtained at states 202 and206, and/or on the comparison made at state 208, a determination is madeas whether ticket prices for one or more seating areas should beadjusted, and if so, by how much. For example, a ticket price adjustmentcan be made to enhance total revenues (e.g., ticket sale and ancillaryrevenues) while not exceeding certain limits, such as limits on priceadjustment frequency, limits on the total number of price adjustments,limits on the price decrease per price adjustment, limits on the totalprice decrease, limits on the price increase per price adjustment,and/or limits on the total price increase.

In certain ticket sales, the foregoing limits may not be applicable orconsidered. For example, if the rules for an event ticket sale precludeincreasing ticket prices for a given quality of seat over time, thencertain limits related to increasing ticket prices may not be relevantas the ticket prices will not be increased. The price adjustmentdetermination can be made using one or more of the models and processesdiscussed herein. Optionally, for a given event, certain ticket pricesmay be adjusted upwards (e.g., for a relatively more in demand venueseat area or quality), and other ticket prices may be adjusted downward(e.g., for a relatively less in demand venue seat area or quality).

Optionally, the scheduling of price adjustments can be specified by theSeller and optionally, the public, or a specified subset thereof (e.g.,members of a fan club, registered users of a ticketing service, usersspecifically placing a request) can be notified when such pricereductions are going to take place, as similarly discussed below.

If a determination is made that tickets prices are to be adjusted (e.g.,for one or more venue seating areas or quality of seats), the processproceeds to state 212, and corresponding ticket prices are appropriatelyadjusted up or down. Optionally, a notification can be provided beforean event ticket sale takes place (if the scheduling of price adjustmentshas already been made) or during the ticket sale (if the scheduling isdone dynamically or is according to a predetermined schedule). Anotification may be provided a predetermined amount of time (e.g., oneweek, one day, one hour) prior to a given price adjustment.

The price adjustment notifications can be provided via a Web page,newspaper advertisements, radio advertisements, televisionadvertisements, mailers, or otherwise. Optionally, notifications can betransmitted to users that requested to be provided with a notificationwhen there is a downward adjustment in ticket prices. Optionally, a usermay specify (e.g., via a Web page form) if a notification is to beprovided to the user if any price drop occurs, if a price drop occurs ina specific seating area (or areas), or if the ticket price falls to orbelow a user specified level. The user can optionally specify if thenotification is to be provided an email, via an SMS message, an instantmessaging message, via a phone call, and/or via other communicationtechniques. If, at state 210, a determination is made that priceadjustments are not to be made at this time, the process proceeds backto state 206.

At state 214, periodically, in response to ticket sales patterns, basedon the time until the event takes place, and/or based on a manualinstruction provided by an operator, a determination is made as towhether tickets for additional seats are to be released for purchase. Iftickets for additional seats are to be released for purchase, adetermination is made as to which seats are being released. The seatrelease determination can be based on instructions manually provided byan operator at that time or previously, at a predetermined schedule(e.g., release specified additional seats on specified dates), or therelease determination can be made by a seat release software module thattakes into account some or all of the historical and real-time datadiscussed above, as well as seat release limits (e.g., limits on howmany seat release offerings can be made for an event, if any).

If the event has taken place (or optionally is to take place within apredetermined time period, such as two hours), or if the limitsdiscussed above prevent further price adjustments, then the processillustrated in FIG. 2 can end without further price adjustments and/orseat releases. Optionally, when tickets are sold, the system can assignseats so as to enhance seat packing, as similarly described above, andthereby enhance revenues.

FIG. 3 illustrates an example process that sets Unit (e.g., ticket)minimum bids in an auction. Prior to an event ticket auction beginning,initial minimum bids are set for one or more categories of tickets, suchas tickets for seats in different venue areas. In this example, at state302 data that will be used for setting initial minimum bids is read froma database or is otherwise received from a data source by a computersystem, such as the system 104 discussed above. In this example, some orall of the following data is accessed:

Historical sales data (e.g., total ticket sales per seating area for oneor more historical auction and/or non-auction event ticket sales, rateof ticket bids per price band/seating area for one or more historicalevents, total ticket sales per price band/area for one or morehistorical events, total number of bids, what ticket brokers have soldtickets for similar events, etc.);

Venue capacity (e.g., total seating capacity for event for which ticketsare being auctioned);

Venue seat configuration (e.g., the number and types of seating areas,the capacity of different seating areas, seat rankings, seating arearankings, etc.);

Cost data;

Price constraints (e.g., maximum ticket price/allowable bid for eachseating area and/or the venue as a whole, minimum initial minimum bidsfor each seating area and/or the venue as a whole, reserve prices,maximum initial minimum bids for each seating area and/or the venue as awhole);

Limits on the minimum bid increment;

Limits on the maximum bid increment;

Limits on the maximum number of tickets a user can bid on;

Limits on the minimum number of tickets a user can bid on;

Limits on the multiple of tickets a user can bid on (e.g., multiples oftwo);

Characteristics of potential ticket bidder population (e.g., populationsize of relevant ticket buying population, motivation for attendingevent, income, predicted or estimated patience, the minimum bid thatwould cause an estimated percentage of the relevant population not tobid on a ticket, and the minimum bid that would cause an estimatedpercentage of the relevant population bid on a ticket);

Limits on the minimum and/or maximum number of seats and/or specifiedvenue areas for which tickets are to be offered in the initial ticketauction offering (e.g., a Seller may specify that at least half theseats and no more than 66% of the seats are to be offered during theinitial ticket auction);

Designation of which seats are to be offered during the initial ticketauction;

The number or percentage of relevant potential bidders who have in thepast opted-in for previous auctions (wherein a bidder has agreed to havetheir bid automatically increased by a certain amount and/or up to amaximum amount in order to attempt to ensure that the bidder will besuccessful in purchasing a ticket in a given auction).

Some or all of the auction set-up instructions discussed above (e.g.,some of the aforementioned limits) may have been set by a Seller,performer, and/or venue operator.

At state 304, some or all of the data accessed at state 302 is entered(e.g., automatically by the computer system or manually by an operator)into one or more pricing models/processes (e.g., softwaremodels/processes), such as those discussed herein, which utilize some orall of the data to set initial ticket minimum bids. If the tickets arefor a general admission event (e.g., where ticket holders are notassigned a reserved seat), a single initial minimum bid may be set forall tickets being initially auctioned. If the event includes reservedseating, then different (or the same) initial minimum bid may be set forseats in different venue areas or price categories/bands.

At state 306, the tickets for seats that are designated to be releasedduring the initial ticket auction are offered for auction (e.g., viaonline Website, phones, kiosks, etc.) to the public or a select subsetthereof. Real time ticket auction and/or sale data (e.g., for completedauction sales) is monitored.

The real time data can include some or all of the following: the numberof active bidders; a current or recent rate of ticket bids (e.g.,optionally, broken down into rate of bids per venue seating area or seatquality category); total ticket bids and/or sales to date (e.g.,optionally, broken down into total tickets bids or sales per venueseating area or seat quality category); current high bid and lowestwinning bid per seating area or seat quality category; remainingcapacity (e.g., for the venue as a whole and/or for specific seatingareas, expressed in absolute numbers and/or as a percentage of totalcapacity); demographic information regarding bidders or purchasers ofevent tickets; and demographic information regarding users that bid on,but did match higher bids for a ticket.

For example, some or all of the demographic information (e.g., age,gender, household income, ticket purchase frequency, event preferences,etc.) can be retrieved from a user's account record using a useridentifier (e.g., a password, userID, token, cookie data, etc.) providedby the user (e.g., manually or automatically by the user's terminal)before or during the ticket selection or purchase processes. Inaddition, the user's Web page navigation information (accessed via acookie or otherwise) can also be accessed and monitored as similarlydiscussed above. In addition, if the ticket auction allows users tospecify the minimum ticket price at which they would submit a bid forone or more tickets, then such information can also be accessed.

Optionally, some or all of the substantially real-time data can bepresented to users, such as bidders via a bid submission Web page orotherwise. Such information can enable bidders to make more informeddecisions regarding when and how much to bid. For example, if there islittle capacity left (e.g., less than 20%) and the bid rate isrelatively high, the bidder may elect to submit a substantially higherbid than would be the case if the bidder was not informed regarding theremaining status and bid rate.

Optionally, the auction (for an event as a whole or for certain seats,such as certain premium seats) may be restricted to a certain categoryof people (e.g., members of one or more specified fan clubs, seasonticket holders, holders of certain types of financial instruments, suchas a credit card associated with a specified brand or issuer, frequentticket buyers, etc.). By way of example, before user can submit a bid,the user may be asked to enter or otherwise provide a password, auserID, token, key, and/or biometric authentication (e.g., wherein theuser's identity is verified via a fingerprint scan, a vein scan, afacial scan, a retina scan, a voice scan, or via other biometricreading). If the user is successfully authenticated than the user cansubmit a bid. If the user is not successfully authenticated, then theuser is optionally prevented from submitting a bid, or the user ispermitted to submit a bid, the bid is not considered and the user is soinformed.

Optionally, a bidder can select (e.g., via a Web page) one or moresubsets of seats or ticket-types for which a bid is to be considered.For example, the bidder can specify that a bid is only for a front rowseat, only for an orchestra seat, or only for an orchestra or centermezzanine seat. Once the bidder submits a bid, the bid is automaticallyinspected to determine whether the bid meets the applicable biddingrequirements (if any). If the bid does not meet such applicable biddingrequirements the bidder is optionally so informed. Optionally, anon-compliant bid is not considered with respect to determining winningbids.

At state 308, some or all of the data collected at state 306 is comparedto corresponding historical data, such as that accessed at state 302. Atstate 310, based on some or all of the data obtained at states 302 and306, and/or on the comparison made at state 308, a determination is madeas whether additional tickets should be released for bid. For example,the determination can be made periodically, in response to ticket salespatterns, based on the time until the event takes place, and/or based ona manual instruction provided by an operator. Optionally, a Seller canspecify the schedule for releasing seat tickets.

If tickets for additional seats are to be released for auction, adetermination is made as to which seats are being released. The seatrelease determination can be based on instructions manually provided byan operator or specified by a Seller, or can be made by a seat releasesoftware module that takes into account some or all of the historicaland real-time data discussed above, as well as seat release limits(e.g., limits on how many additional seat release offerings can be madefor an event, if any).

At state 312, new minimum bids, which can be higher or lower than thosefor comparable seats already released for auction, are calculated oraccessed from memory for the tickets that are to be released. Forexample, a minimum bid adjustment can be made to enhance total revenues(e.g., ticket sales and ancillary revenues) while not exceeding certainlimits, such as limits the minimum permissible minimum bid or limits onthe highest permissible minimum bid. At state 314, the additional seattickets are released for auction with the corresponding minimum bidsspecified. The process then proceeds to state 306. If, at state 310, adetermination is that additional tickets are not to be released for bidat the current time, the process proceeds to state 306.

Optionally, a user may specify (e.g., via a Web page form) if anotification is to be provided to the user if a minimum bid is at orlower then a certain level, or when a minimum winning bid reaches acertain level for a specific seating area or areas (e.g., a certaindollar amount or when it exceeds the user's current bid). The user canoptionally specify if the notification is to be provided an email, anSMS message, an instant messaging message, via a phone call, and/or viaother communication techniques. If the event has taken place (oroptionally is to take place within a predetermined time period, such astwo hours), or if the limits discussed above prevent additional priceadjustments, then the process illustrated in FIG. 3 can end.

Once the auction has ended, the ticket assignments to bidders can becompleted, and the ticket prices calculated. Optionally, the auctionwinners can pay what they bid, all winners can pay the lowest winningbid for a category of seats (uniform pricing), the auction winners canpay a calculated “average” (or other statistical measurement) acrosstheir bid values, as similarly discussed above.

If a reserve price (e.g., the minimum price that will be accepted for anitem) was set for tickets for one or more seats or categories of seats,then a determination is made as to whether one or more bids for thecorresponding tickets attained the reserve price. If, the reserve pricehas not been attained, then optionally, those tickets will not be soldto a bidder. Optionally, the reserve price may not have been disclosedto bidders prior to the auction close. Optionally, the reserve price mayhave been disclosed to bidders from the time the tickets were put up forauction. Bids are optionally accepted even if the reserve has not beenattained.

FIGS. 8A-C illustrate example auction user interfaces. The userinterfaces can be accessed from a ticketing web site via a web browser.The user interface illustrated in FIG. 8A, provides fields via which theuser can specify for which ticket groups (e.g., seating areas) a userbid is to be considered in an event ticket auction. In this example, thefirst listed group is considered the most desirable listed ticket group,the second listed group is the second most desirable listed ticketgroup, etc.

The user call select an “all” control if the user wants the bid competein all listed ticket groups. The user can instead manually select one ormore of the listed ticket groups. In the illustrated example, the userhas selected the third, fourth, fifth, and sixth row (Ticket Groups1-4). The user can click on an arrow following the ticket groupdescription and the system, via the user interface, will displayadditional information regarding the ticket group. For example, thedescription for the third row ticket group discloses that the groupincludes the chance to win reserved seats on the floor, row 3, seats1-8. In addition, the user interface provides the current minimum bidfor the corresponding ticket group. The user interface explains that inthe example auction, the more ticket groups selected by the user thebetter the chances the user will win a ticket, thus encouraging the userselect several ticket Nice Day”), the venue (“Air Canada Centre,Toronto, ON), and the date and time of the event. The user interfacealso lists the auction start date/time, end date/time, and timeremaining in the auction.

The example user interface illustrated in FIG. 8B lists the ticketgroups previously selected by the user and the current minimum bid. Acontrol (e.g., an “Add More Ticket Groups” link) is provided via whichthe user can add additional ticket groups (e.g., using a user interfacesimilar to that illustrated in FIG. 8A.) A “Bid Per Ticket” field isprovided via which the user can enter the amount the user is bidding foreach ticket requested by the user via a “Ticket Quantity” field. In thisexample, the user is informed that bid increases are restricted tomultiples of $10. An opt-in field is provided via which the user canrequest that a notification (e.g., an email notification) be provided tothe user if the user's bid status changes (e.g., from winning tolosing). The user can activate a “Submit” control to submit the bid.

FIG. 8C illustrates an example bid status user interface. The userinterface displays the user's current bid per ticket, the ticketquantity designated by the user, and the total bid amount (e.g., theticket quantity multiplied by the user's current bid per ticket),optionally, excluding service fees. The user interface displays the bidstatus (e.g., outbid, winning, etc.) for each of the ticket groupsselected by the user. In the illustrated example, the user has bid $100per ticket for a ticket quantity of four. The user has been outbid forthe Ticket Group 1 (third row ticket group), where the current minimumbid is $110, and is winning in Ticket Group 2 (fourth row ticket group),where the current minimum bid is $80. Because, in this example auction,the user will be awarded tickets from the highest ranked ticket groupfor which the user's bid is a winning bid, optionally, the status of

A control (e.g., an “Add More Ticket Groups” link) is provided via whichthe user can add additional ticket groups (e.g., using a user interfacesimilar to that illustrated in FIG. 8A.).

The user is informed that the user needs to increase the user's bid towin seats in Ticket Group 1. A field (“Increase Bid Per Ticket to”) isprovided via which the user can specify a new bid. The user can click ona “Calculate” control and a new total bid amount will be calculated bythe ticket system or on code executing on the user's computer, and newtotal bid amount will be displayed to the user. The user can thenactivate a “Submit” control to submit the new bid. Once the auction isconcluded, the user will be awarded tickets from the most preferredticket group (e.g., highest rank ticket group) for which the user's bidwas a winning bid (if any).

FIG. 9 illustrates an example of a user interface that is optionallydisplayed to a user that has a ticket-related request pending in a queue(e.g., a queue that includes ticket requests being provided over anetwork, such as the Internet, from user terminals). In this example,the user is informed regarding how may ticket requests (or an estimatethereof) are ahead of the user in the queue. The user interface furtherinforms that user regarding the likelihood that the user will be able topurchase tickets via the user's queued request. In this example, theuser is informed that the system estimates that the event will be soldout before the user's request is serviced. The user is provided theoption of maintaining the user's place in the queue, of selecting analternate performance date, and selecting a performance by a differentperformer. The selection of performances by a different performer isoptionally automatically made using predictive collaborative filtering,as similarly described above.

FIG. 10 illustrates an example user interface via which a user canspecify which types of notifications the user wants to receive relatedto an event ticket sale. In the illustrated example, the user interfaceincludes a performer name, an event venue name, and a date (andoptionally a time) for an event associated with declining ticket prices,wherein ticket prices for at least one category of seats may be reducedprior to the event occurring. The user is provided with severalnotification options which the user can select by clicking on a clickfield or otherwise. In this example, the user can request that the userbe provided with a notification when a ticket price drop occurs.Optionally, a field may be provided via which the user can request thata notification be provided when a price drop occurs for a specificticket group or groups.

With reference to FIG. 10, a field is also provided via which the usercan specify that a notification is to be transmitted to the user when aticket price attains or falls below a price specified by the user. Theuser can further request that a notification be provided when additionalseats are released for the event. Optionally, the user can specify thatthe seat release notification be provided only when seats are releasedin a specified venue area. Fields are provided via which the user canselect one or more notification destinations or methods (e.g., emailaddress, SMS address, Instant Messaging address, phone number, etc.).

FIG. 11 illustrates an example survey form which can be used to collectinformation from users (e.g., likely ticket purchasers or the generalpublic) which can be used to price tickets. The survey can be presentedin the form of a web page to a user in the process of searching for,selecting, or purchasing a ticket, via an email to a registered user ofa ticketing service, or otherwise. The survey results can be stored in asurvey database, as described above.

The example survey illustrated in FIG. 11 relates to a specificperformer, but other embodiments can relate to one or more event types(e.g., for rock concerts generally, for sporting events, for plays,etc.) rather than a specific performer. In the illustrated example, theuser is asked to submit how much the user would be willing to pay fortickets for seats of different quality in different venue areas. Theuser is also asked to provide an indication as to what the user thinksthe relative value difference is for different seating areas. Aspreviously discussed, this information can be used to set ticket pricesfor one or more events. Optionally, all or a portion of the surveyresults aggregated from a plurality of users can be presented to one ormore potential ticket purchasers to provide additional valuationinformation.

FIG. 12 illustrates an example user interface that enables a user toelect to participate in an event presale upon payment of a presaleparticipation fee. In the illustrated example, the user can elect to paya first amount ($20) to participate in a first, relatively earlier phaseof a presale (e.g., the first week of the presale), or the user canselect to pay a relatively lower amount ($10) to participate in arelatively later phase of the presale (e.g., the second week). In thisexample, the presale participation fee is not refundable and optionallywill be charged or billed to the customer (e.g., charged to the user'scredit card) even before the presale takes place. Thus, the user ischarged the presale participation fee even if the user does not laterpurchase a ticket. Optionally, the presale participation fee is insteadrefundable.

An example software system and associated user interfaces will now bedescribed that can be used for forecasting ticket demand and for yieldmanagement. The forecasting/yield management software system (alsoreferred to as the yield management system) utilizes historical datastatistics from past events to forecast expected demand for futureevents. The yield management system can further be used to estimate andenhance or optimize expected cash flow for an event. Yield managementcan assist Sellers in establishing and/or adjusting pricing and capacityof an event, and optionally can aid in selecting and initiatingpromotions to increase ticket sales so as to enhance or maximize eventincome.

As will be described in greater detail herein, the software systemperforms demand forecasting by importing historical demand data andstatistics. For example the system can import (e.g., from one or moregenerated reports) rates of ticket sales from the time tickets for anevent are offered for sale until the sale is completed (e.g., up to theevent data). By way of example, a report can include eventcharacteristics, such as time of day, day of week, primary performer,and secondary performers. By way of further example, a report caninclude a season of events, such as the Dodgers' 2006 season, or aportion of a season. Optionally, multiple reports, or multiple seasonsworth of events, can be imported.

Optionally, a user can set up a filter to locate and identify eventsthat have characteristics that correspond to the filter. For example,the filter can be set up to locate historical events withcharacteristics similar to the event for which demand forecasting isbeing performed. The historical data and statistics associated with thelocated event or events can then be used to generate demand forecasts.

For example, a user can set up a filter to filter for evening Dodgers'games, where the team plays the Padres. Demand forecasts created underthis filter estimate future evening Dodgers' games, where the Dodgersplays the Padres.

A user can further specify that certain events (e.g., a season ofevents) be weighted differently than other events when generating demandforecasts if certain facts indicate such weighting is appropriate. Forexample, if a team had a winning season in 2004, a losing season in2005, and is expected to have a losing season in 2006, it may beappropriate to assign a higher weight to the 2005 data than to the 2004in forecasting demand for the year 2006 season.

The yield management system can optionally enable a user to map/overrideevent characteristic values. By way of example, a user can specify thatcertain event characteristics that differ are to be mapped so as to beconsidered the same for filtering purposes. For example, sales demandfor a Padres game on Memorial Day (Monday), may be more similar to thesales demand for a weekend game (Saturday or Sunday) than a typicalweekday game; in this scenario, a user may remap the event's “day ofweek” characteristic to a Saturday or Sunday. Thus, the system willidentify historical events that took place on Saturdays and Sundays whengenerating for an event that is to take place on Monday (e.g., MemorialDay).

A user can also override for a default characteristic, which mightinclude a Secondary Performer. By way of example, assume that the eventbeing forecast is for a first team playing against a second team, wherethe second a team was popular for the previous two years ago but sincethen many of the better players have left the team. It may beappropriate to use historical event information for games involving thefirst team and a relatively unpopular third team rather than, or inaddition to, historical event information for games including the firstteam and the second team.

After historical data and statistics have been imported, an event filterhas been created, seasons have been weighed against each other, andcharacteristics have been overridden (where appropriate), the tool cangenerate a forecast of expected sales demand. For a future event whosecharacteristics correspond to those identifier via the filter, thedemand forecast optionally includes an expected sales rate andaccumulation of sales leading up to a performance date or end of saledate.

The forecast information can be used for tracking and planning purposeswith respect to yield enhancement. For example, if actual sales arebelow forecasted sales, a promotion (e.g., a ticket price reduction, twotickets for the price of one, free parking, coupon for free ordiscounted concessions) may be instituted to increase sales.

In addition to, or instead of, the demand forecasting processeddescribed above, the management system can forecast demand usinginterpolation to forecast sales/demand for an event based at least inpart on data and statistics of an event with characteristics which arenot very similar to those of the event that is being forecasted. Suchinterpolation can be advantageously used when certain characteristics ofthe event to be forecasted do not match characteristics from the set ofimported events.

An example cash flow analysis and enhancement/process performed by theexample system operates as follows. One or more individuals or groupsprovide an estimation of expected demand vs. ticket pricing. Demand vs.pricing estimates are optionally broken down by price level and/orseating area, and the system provides one or more methods to shape andscale demand vs. price curves. These estimates represent a “vote” forexpected demand and price, and different estimates can be weighedrelative to each other to produce a combined demand vs. price estimate.

For example, a Seller accounting department and the general manager forthe event's market may provide demand vs. pricing estimates. If it isbelieved that the accounting department generally provides more accurateestimated than the general manager, the estimate provided by theaccounting department is optionally weighted more heavily in a demandvs. price curve that is a result of the combination or aggregation ofthe account department estimate and the general manager estimate.

In the example below, the Box Office and the Accounting departmentprovide estimates. If the Box Office' estimate is believed to be (e.g.,based on prior estimate performance) twice as likely to be correct asthe Accounting's estimate, then the Box Office can be assigned a weightof 2 and Accounting a weight of 1.

Name Weight Type Accounting 1 User Box Office 2 User Test 0 User

For example, the accounting department can estimate likely demand for:

$10 seats is 1000

$20 seats is 500,

$30 seats is 400 seats.

For example, the box office can estimate likely demand for:

$10 seats is 500

$20 seats is 250,

$30 seats is 200 seats.

The system can extrapolate values for demand at various prices using alikely or average demand curve, a pessimistic demand curve and/or anoptimistic curve.

The estimates from the box office and accounting department are combinedor merged using the defined data source weighting. Thus, if theaccounting department estimates there will be a 1000 people that willcome at $10. and the box office estimates there will be 2000 people, andthe box office is assigned a weight double that of accounting, then themerged demand estimate is about 1600, and this merged estimate can beused in forecasting demand.

The system can generate a net cash flow estimate based on the estimateddemand and on assumed pricing and capacity inputs. Thus, for example,pricing and capacity are optionally set on a per-price level basis.

The scheduling or itemization of expected cash flow will now bedescribed. In an example embodiment, items in a schedule representeither a positive or a negative cash flow. Schedule items can be groupedinto categories and optionally sub-categories, such as “Concessions”,“Ticketing”, or “Facilities”. The cash flow amount associated with agiven item is optionally specified as a fixed cost for the entire event,or is dependent upon pricing, demand, occupancy, revenue, and/orcapacity information, optionally based on the historical event dataobtained as described above. Cash flow amounts can optionally also bespecified as a single value for all price levels, or can be split intoseparate values for different price levels. Several example cash flowschedule items are described below.

By way of example, negative cash flow can be associated with janitorialservices. Janitorial services may be assigned to the “Facilities”category, and optionally a “janitorial” sub-category (another example“Facilities” subcategory can include event security). The negative cashflow association with janitorial services for an event can optionally beset up as a fixed charge for the entire event, or the janitorialservices negative cash flow can be dependent upon the number ofoccupants (e.g., number of seats to clean).

By way of example, positive cash flow can be associated with ticketingsurcharges. Surcharges could be assigned to a “Ticketing” category, andin particular, to a “surcharge” sub-category. Surcharge amounts,expressed as a percentage, can be dependent upon expected ticketingrevenue, such as those determined using historical event data, asdescribed above. By way of example, if a first price level has a highersurcharge than a second price level, a per price-level split ofsurcharges can be provided.

In addition, the system can perform “what-if” analyses andoptimizations. For example, a schedule of cash flow items can befiltered to only display items in a single category or sub-category, orin two or more specified categories or sub-categories. In may bedesirable to limit the analysis to a single category or sub-category tosupport a contract negotiations, for example, where ticket surchargeinformation is relevant, but costs for janitorial services are not.Selecting a category causes a cash flow schedule, such as thatillustrated in FIG. 23, to limit the display to items and resulting cashflows in the selected category (and its sub-categories, if there aresub-categories). For example, selecting a ‘Facilities’ category, wouldcause the cash flow schedule to restrict the display to items in the‘Facilities’ category, and corresponding subcategories (e.g., ‘eventsecurity’ and ‘janitorial’ sub-categories).

For a given cash flow schedule, ticket pricing can be optimized tomaximize or enhance net cash flow. As part of what-if analyses,optionally ticket pricing can also be set manually. In addition, seatingcapacities can be shifted from one price level to another price level aspart of the analysis and yield enhancement

Optionally, selected demand estimates can be excluded from cash flowestimates. In terms of resulting expected cash flow, this type ofanalysis highlights the differences between several demand estimates.

While preserving the same overall shape, demand vs. pricing curves canbe scaled in order to demonstrate the sensitivity of expected cash flowto expected demand.

FIG. 13 illustrates an example yield management system event demandforecast user interface. In this example, the user interface is dividedinto four parts so as to help guide the user though the four examplestates of a yield forecast process. In this example, the user begins atstate 1 and proceeds through state 4.

In particular, with respect to state 1, accessing historical eventstatistics and data, the user can first activate an import control(e.g., an “Import Historical Season Data” control) to import historicaldata for one event or for a set of events.

With respect to state 2, the user can then activate eventcharacteristics controls. By way of illustration, a user can define newcharacteristics or override existing characteristics. For example, theuser can activate a “define/select characteristics” control to defineand select event characteristics. The user can also, activate anoverride characteristic control to override characteristics, ifdesirable or appropriate.

With respect to state 3, event filtering can be performed. A “selectevents” control is provided (e.g., a “Select Events in Forecast”control) via which the user can set up one or more event filters. Aclustering control is provided (e.g., “Characteristic Clustering”) whichenables a user to select a cluster of events, for example a cluster ofthe best selling events, and observe whether there is a commonality or apattern to such events. For example, a pie-chart can be generated anddisplayed, with the relative number of characteristic values for eachselected event in an event demand forecast. By way of illustration, anillustrative chart may indicate that 5 of the selected events are 7:05PM games, and 1 game is a 4:05 PM game, which illustrates graphicallythat most of the selected games/events are evening games.

At state 4, forecast generation is performed. In this example, apre-event sales rate control is provided that, when activated, willcause the system to generate a prediction of sales over time (e.g., agraph format with a sales vs. time curve, and/or in a text format). Anestimate future sales control is provided that, when activated, willcause the system to generate an estimate of future sales for one or moreevents.

Other embodiments can include more, fewer, or different controls, whichcorrespond to more, fewer, or different yield forecast process statesthan those discussed above with respect to FIG. 13.

An example of the use of the Event Demand Forecast user interface willnow be discussed. In this illustrative example, the user wants topredict the demand for a particular sports event for the current seasonusing data from the last two seasons. The user will import historicaland other data (if such data needs to be imported), optionally addand/or remove characteristics, optionally override characteristicvalues, perform event filtering (e.g., select events to be include inthe forecast and optionally view characteristic clustering), generate aforecast result report (e.g., display a demand forecast graph (Pre-EventSales Rate), and/or estimate future sales). For example, if the eventbeing forecast is for a sporting event between two teams on a givendate, the report can provide a demand estimate (e.g., seats expected tobe sold per price level) for the sporting event based on historicaltrends. A dollar value can also be provided for expected ticket sales,concession sales, and/or total revenues. Optionally, the report can takeinto account cost data and provide an estimated net revenue orprofitability estimates.

In particular, with respect to importing historical data, in thisexample, the historical data is imported on a seasonal basis. Forexample, a report can be generated that includes a season of events,such as a baseball season or a concert tour, and the report results canthen be imported (or directly accessed). Optionally, multiple reports,representing multiple seasons' worth of events, can be imported into theyield management system.

By way of illustration, statistical reports can be generated based inwhole or in part on past events and the report results can be importedinto the yield management system. The data collected by the report caninclude some or all of the following:

event code;

event attributes;

capacity;

ticket count, optionally broken down by price level;

hourly, daily, weekly, and/or monthly ticket sales broken down byoperator type; and

hourly, daily, weekly, and/or monthly ticket sales broken down by saleschannel (e.g., online Website, physical ticket outlets, phones, kiosks,box-office, etc.).

The import process can optionally be performed as follows. The user canclick the Event Demand Forecast tab to access the corresponding controls(e.g., buttons or links). The user can then click the Import HistoricalSeason Data control, and an import form (e.g., an Import HistoricalEvent Statistics dialog box) is displayed on the user terminal. FIG. 14illustrates an example data import user interface that can be used toimport data (e.g., event type, event data, venue identifier, venuecapacity, sales data) corresponding to historical events and/or data forfuture events (e.g., event type, event data, venue identifier, venuecapacity).

Referring to FIG. 14, the user can utilize a file selection userinterface (e.g., a File to Import From field) to select a file to beimported, such as a report regarding one or more historical eventsgenerated as discussed above. The user can then click on a control toselect a type of import (e.g., History Events to import events and salesdata, or Future Events to import event data). The user can then enter aname to be associated with the imported reports (e.g., a unique seasonname, such as one that includes the season year(s)) and activate animport control to initiate the import, which will then be performed bythe system. The foregoing process can be repeated to import one or moreadditional sets of data.

After importing data as described above, the user can activate thedefine/select characteristics control illustrated in FIG. 13 to defineevent characteristics, and one or more user interfaces, such as thoseillustrated in FIGS. 15A-C, are presented via the user terminal. A setof characteristics/attributes are associated with a given event. Thesecharacteristics are included in or related to the historical dataimported to the yield management system. For example, thecharacteristics can include predefined characteristics orcharacteristics that are defined during the import process, such as someor all of the following: Primary Performer, Secondary Performer,Performance Date, Day of Week, Time of Day, Major Category, MinorCategory, Venue, and Prototype Curve. A prototype, as used herein, isset of data, which acts as a standard or reference that other event datacan be compared to.

Optionally, the user may create user-defined characteristics. Theuser-defined characteristic may be related or unrelated to existingcharacteristics. For example, a user may define a new characteristicwith values of “weekday” and “weekend”. Such characteristic can becreated based on the day of week characteristic. For example, eventswith a “day of week” value of Monday, Tuesday, Wednesday, Thursday orFriday will be assigned a value of weekday for the new characteristic.Events with a “day of week” value of Saturday or Sunday will be assigneda value of weekend. FIGS. 16A-B illustrates an example weekend/weekdayscharacteristics and values user interface via which a user can mapcharacteristics, select prototype curve values, and select “day of week”values.

By way of further illustration, another example user-definedcharacteristic can be “baseball special promotions” with values such asno promotions, bat day, baseball card day, and so on. This user-definedcharacteristic can optionally be a stand-alone characteristic (e.g., notbased on or related to other event characteristics), and optionally, thecharacteristic value for a given event is entered manually or otherwise.

Users are optionally also provided with the ability to addcharacteristics, modify characteristics, edit characteristic values, ordelete characteristics via appropriate controls, such as the “AddCharacteristic”, “Delete Characteristic”, and “Edit Values” controlsillustrated in FIGS. 15A-B. Thus, for example, if a specificcharacteristic does not contain relevant information for forecastingpurposes, the user may choose to delete that characteristic. An examplemay be the venue characteristic for a sports team (although in manyinstances, the venue characteristic may be relevant for a sports team).FIG. 15C illustrates a user interface displayed when the user activatesthe edit characteristic control. The user can select the characteristicto be edited from a current table (e.g., “Primary Performer”), select acharacteristic type, and select from one or more allowed characteristicassignments (e.g., one or more baseball teams).

Characteristics can also be used to visually identify event patterns andcommonalities. For example, a user can instruct the system to generate agraph of imported historical sales data. The user can then select acluster of events, for example a cluster of the best selling events, andfind out if there is a commonality or a pattern to the best sellingevents. By way of illustration, for a sports team, it may be one or moreparticular opposing teams that appear to cause sales to significantlyincrease. For a concert tour, it may be a specific venue or a day ofweek (e.g. Saturday) that tend to be associated with the best sellingevents or that tend to coincide with increased ticket sales forlike-priced tickets.

In addition to enabling users to assign characteristics to events, theuser interfaces illustrated in FIGS. 15A-B enable a user to assignoverrides because of external circumstances or where otherwiseappropriate.

An example characteristic creation process will now be described,although other processes and states can be used.

-   1. The user can activate the Define/Select Characteristics control    from the user interface illustrated in FIG. 13. A table of the    existing characteristics and their corresponding values, such as    that illustrated in FIG. 15A, is then displayed to the user.-   2. The user can activate the Add Characteristic control illustrated    in FIG. 15A. A new characteristic will be displayed (e.g., at the    bottom of the table). The user can activate (e.g., double-click) the    name to place the cursor in the name field, delete the default name,    and type a new name.-   3. The characteristic type is displayed as <Undefined>. The user can    click on a drop down list arrow or other menu to display a list of    data types (e.g., Number, Date, Time and Text) which the user can    then select.-   4. The user can click the Edit Values control.-   5. The can user position the cursor in a blank Values field and    click an Add control (e.g., via a right-click menu selection).-   6. The user can then type the desired value.-   7. The user can repeat steps 5 and 6 until the values of the    characteristic have been appropriately defined.

To assign values to events for a new related characteristic, the userproceeds to steps 8 and 9. To assign values to events for a newstand-alone (unrelated) characteristic, steps 8 and 9 can be skipped,and the user performs an assignment of characteristic values to eventsfor a new, unrelated characteristic.

-   8. From a Mapped Characteristics list, such as that illustrated in    FIG. 16B, the user selects the check box for the characteristic that    corresponds to the newly created characteristic, and the values for    that characteristic will be displayed.-   9. From the new characteristics Values box, the user selects check    boxes for corresponding characteristic values.

If the user is assigning characteristic values to for a new unrelatedcharacteristic, the following process can be performed.

-   1. The user activates the Override Characteristic Values control    illustrated in FIG. 13. A table of events and corresponding    characteristic values is displayed.-   2. The user can select a default value, <Undefined>, for the new    characteristic, the user can click on a drop down list arrow or    other menu to display a list of values from which the user can then    select the appropriate value.-   3. If more than one season of data has been imported, the user can    select the next season and from the new characteristics Values box,    select check boxes for corresponding characteristic values.

With respect to deleting a characteristic, the user can select acharacteristic (e.g., via the table illustrated in FIG. 15A) to bedeleted, click on the delete control, and the characteristic will bedeleted.

A user can optionally specify how many events are to be used to generatea demand forecast. Many events have ticket sales that generallycorrespond to one of the following curve-types, which can be used aprototype curves, although some events may have ticket sales thatcorrespond to different curves, such as a combination of one or more ofthe following curves.

Curve A: The first portion of the curve corresponds to a high salesrate, which then tapers off, reflecting an initial high sales rate, withthe sales rate dropping off over time.

Curve B: A relatively flat curve, indicating that sales are relativelyconstant over the course of the ticket sale or a substantial portionthereof.

Curve C: The first portion of the curve is relatively flat, with thecurve then ascending, indicating that the initial sales rate isrelatively flat, but that the sales rate increases as the eventapproaches.

Curve D: The first portion of the curve corresponds to a high salesrate, which then drops to about zero, reflecting a high sales rate untilthe event is sold out.

Thus, if an event is sold out, it may correspond to Curve D. If theevent is 90% sold out it will be a different curve type, and if theevent is 30% sold out, the curve is likely to also be different.Optionally, the system can display at the same time the sales vs. timecurves for a plurality of selected historical events. The user candeclare (e.g., based on final sales), how many events the user considersto belong to the same category or curve type. The system optionallycolor codes event curves based on user-specified criteria. For example,the user can declare that the ninety-nine events with the highest ticketevents belong to a first category (e.g., are Curve A-type events) andthat such curves are to be color coded red, and that the events ranges100-200 by sales belong to a second category and that such curves are tobe color coded red.

The user can also specify that an average demand curve over time is tobe displayed for events belonging to the first category, the secondcategory, and/or a combination of the first and second categories. Theactual or relative sales rates of a current event can be observed andcompared and fit against a prototype curve (e.g., an average demandcurve and/or one or more of the individual event curves), and aprediction can be made regarding future sales for the current event.

As previously discussed, a user can set up one or more events filters toidentify and select appropriate historical data to be used in demandforecasting, yield management, or otherwise. A filter can be definedwhich to locate historical events that have a set of characteristicsthat correspond to the event to be forecasted.

For example, if data for an entire season were imported, there may bespecific events that the user believes may more closely represent theevent to be forecasted. Thus, the user can use the filter to includethose more representative events, rather than the entire season, inperforming a forecasting analysis. Optionally, the user can assignweights to data and override characteristics.

Optionally, a table can be presented to the user that lists one or moreimported events and optionally, the characteristics (e.g., PrimaryPerformer, Secondary Performer, Performance Date, Day of Week, Time ofDay, Major Category, Minor Category, Venue, and Prototype Curve)associated with corresponding events. Optionally, each event (or set ofevents, such as a season of events) have an inclusion check box viawhich a user can mark an event or set of events for inclusion in theforecasting analysis, and an exclusion check box via which a user canmark an event or set of events for exclusion from the forecastinganalysis. Optionally, the table can be sorted by clicking on a columnheader, which can aid in grouping events. For example, if the PrimaryPerformer is the Dodgers, the user can click on the Secondary Performerto sort and group the events in which the Dodgers play a particular team(e.g., the Giants) together. The user can then easily select historicalgames in which the Dodgers played the Giants. Optionally, multipleseasons (or other grouping) of event statistics can be selected forinclusion or exclusion from the event demand forecast. Optionally, auser can activate a “select all” control to select all listed events forinclusion in the forecast analysis, or the user can activate a “clearall” control to unselect currently selected events. The user canactivate a filter control (e.g., “Filter Events In Forecast” button)which causes one or more database queries to be generated thatcorrespond to the filter.

Optionally, instead of or in addition to using the table above to selectof filter events, a user can view event demand graphs of multiple eventsand select (e.g., via a lasso or user drawn selection tool, wherein auser can draw out an irregularly or regularly shaped area with apointer, and the objects inside this area are selected) a certaincluster in the graph, and the historical data corresponding to theselected cluster or events will be used to generate a demand forecastfor the event at issue. Optionally, in addition or instead, the user canform a database query for the characteristics of the past events thatthe user expects to see in the event at issue.

For example, a user might filter for evening Dodgers games, where theopposing team is the Padres. A table will display Dodgers night gamesagainst the Padres. Demand forecasts created under this filter wouldforecast demand for future evening Dodgers vs. Padres games.

FIG. 18 illustrates an example user interface for defining a filter. Inthis example, the user has set up the filter to select events whereinthe secondary performer characteristic (e.g., the visiting team) equalsthe Milwaukee Brewers, and the “time of day” characteristic equals 7:05PM. The user has named the filter “Brewers Night Games.” The desiredrelation between the characteristic and the characteristic value can beset using a desired comparison parameter (e.g., equal to, not equal to,great than, less than, etc.). When the filter is applied, the systemwill locate and display events in which the Brewers played, scheduledfor 7:05 PM. The user can later use the saved filter without having toredefine the filter. This example filter would be appropriate forforecasting what the pre-event sales for evening Brewers games mightlook like for the next season.

As previously discussed, the system enables a user to override/remapcharacteristic values. A user can select the mapped characteristic,select the desired value, and select a “Use Override CharacteristicValues in Forecast” instruction. The system will then use the overridevalue. FIG. 19 illustrates an example characteristic override userinterface. The user can select a desired event season or seasons via aselect season menu. A user can click on or otherwise select acharacteristic value (e.g., the Philadelphia Phillies) and enter amapped-to value. In this example, a user selected the PhiladelphiaPhillies for entry EPM0425, and selected or typed in the San FranciscoGiants as the mapped-to characteristic value. The user interfaceindicates which value is the actual value and which is the remapped ormapped-to value. For example, although the Florida Marlins may actuallybe playing the Philadelphia Phillies for a given game, the user caninstead specify that a game in which the Giants are playing is to beused in generating a forecast.

As previously discussed, a user can request a demand forecasting report,such as Pre-Event Sales Rate report. The report can be in the form of atime trend analysis that uses historical event data to predict sales forfuture events based on selected events with characteristics similar tothose of the future events. The time trend analysis can be presented inthe form of a graph of forecasted daily cumulative sales (e.g., as apercentage of the final sales and/or in absolute numbers). Forecastinformation can be used for tracking and planning purposes. For example,if actual sales are below forecasted sales, a promotion (e.g., adiscount, two-for-one sale, free parking, free or discountedconcessions, upgraded seat, etc.) may be implemented by the Seller toincrease sales.

By way of example, based upon a set of selected events, a pre-eventdemand forecast view displays a forecast of expected sales leading up tothe performance date. One axis optionally represents the cumulativesales, optionally as a percentage of final sales, and a second axisrepresents the number of days (or other time period, such as hours)before the event. The graph can display demand or other appropriatestatistical curves for events used in generating the forecast(optionally, with a first color coding or other identifier), events notused in generating the demand curve (optionally, with a second colorcoding or other identifier), and a demand curve (forecasted average ofexpected or estimated cumulative sales) for the event being forecasted(optionally, with a third color coding or other identifier). The termcurve as used herein is not limited to smooth curves, and can alsoinclude jagged curves, and curves with ninety degree or other points ofinflection. By way of further example, several different graph types canbe used to display demand curves. For example, a linear graph tries tofit a line through the curves, an exponential graph tries to fit anexponential decay curve through the data points, etc.

FIG. 20 illustrates an example event demand forecast user interface. Theuser interface presents several categories of color-coded (or otherwiseidentified) demand curves. For example, the illustrated user interfacepresents curves for historical event data not being used in the currentforecast, for historical event data being used in the current forecast,and for a predicted demand curve for the event being forecast. In thisexample, the X-axis corresponds to cumulative sales percentages relativeto the total event sales (e.g., 100(sales to day/total sales)). TheY-axis corresponds to the number of days (or other specified timeincrement) before the event. Optionally, instead, the X-axis cancorrespond to cumulative sales percentages relative to the total numberof event tickets offered for sale or event capacity. Optionally,instead, the X-axis can correspond to cumulative sales, rather than as aratio or percentage. As the user moves a cursor (e.g., using a pointingdevice) over a portion of a demand curve, the system causes the userinterface to display the corresponding cumulative sales as a percent oftotal final sales, the corresponding cumulative sales as a percent offinal event capacity, the actual sales per capacity basis, and thecorresponding days before the event, in corresponding user interfacefields. The user can specify the final capacity basis via a finalcapacity basis field.

Optionally, a user select (e.g., via a lasso, selection rectangle, oruser drawn selection tool) a cluster of interest in the graph, and thehistorical data corresponding to the selected cluster or events will beused to generate a pie chart, such as that illustrated in FIG. 21. Theexample pie chart in FIG. 21 indicates that 5 of the selected events are7:05 PM games, and 1 game is a 4:05 PM game, which indicates that mostof the selected games/events in the cluster are evening games.

As previously discussed, if a user observes a group of curves on thegraph which appear to be clustered, the user optionally can visuallyselect the clustered curves via a lasso or otherwise. Eventscorresponding to the set of selected curves will be selected and theevent demand forecast will be re-generated based upon this selection.After selecting curves in this fashion, a user interface displayingcharacteristic value clustering is optionally performed.

Optionally, demand forecasting can be performed using interpolation topredict sales/demand for an event, even if the event's characteristicsdo not match those of an historical event. This type of analysis isoptionally performed when certain characteristics of the event to beforecasted do not match or do not sufficiently match characteristicsfrom the set of imported historical events. Interpolation can be usedinstead of, or in addition to selecting a group of events to produce aforecast for an event whose characteristics “look like” thecharacteristics in the selected group of events.

Thus, a system and process has been described that provides for:

1. Importing historical demand statistics

2. Selecting and/or filtering events with characteristics similar to theexpected event

3. Weighting different events of event seasons of statistics

4. Overriding characteristics

5. Producing a demand forecast

6. Viewing characteristic value clustering

7. Predicting sales/demand for an event using interpolation

Optionally, a value (e.g., a dollar value or a relative value) can beassigned to one or more seats or seating areas. Once seat ticket pricinghas been determined based on the yield management processes describedabove or otherwise, a comparison can be performed with the assignedvalue. If the pricing and the values differ, or differ more than acertain amount or percentage, it implies that the values are not correctand/or the system has misestimated the demand at one or more pricelevels, and the values and/or the pricing can be adjusted accordingly.

Optionally, the system can track the effect of one or more promotions ondemand, revenues, the net cost of the promotion, and the net incomerelated to the promotion. Optionally, a report can be generated thatprovides the event cost without the promotion, and the event costincluding the promotion, and the net revenue can be determined.

FIG. 22 illustrates an example user interface for setting up a cash flowanalysis, such as a new cash flow analysis. Creating a new cash flowanalysis, which provides a cash flow estimates, sets up an initialdemand estimate and a cash flow schedule. In addition, a user specifiesone or more analysis parameters. The user can specify the source(s) ofthe initial demand estimate(s), the initial cost/income schedule, thenumber of event price levels, a minimum price, and a maximum price,which then be used in generating a cash flow prediction. For example,price levels can play a role in demand estimation, ticket pricing, andsplitting of cash flow items by price level. The minimum and maximumprices provide a lower and upper bound for demand estimations, so thecash flow analysis software can determine whether sufficient informationhas been provided to accurately approximate, or interpolate, expecteddemand for one or more prices between the minimum and maximum pricelevel or at the minimum or maximum price level.

FIG. 23 illustrates an example cash flow schedule. A cash flow schedulerepresents an itemization of the expected costs and income associatedwith an event. A given schedule item contributes positive or negativecash flow to an event's overall net cash flow. Cash flow for anindividual schedule item is optionally determined by a rule input value(e.g., face value, security, post-event clean up, performer fee, foodand drink, performer's share of the concessions, surcharge to ticketingsystem operator, etc.) and rule type (e.g., fixed amount, per ticketsold, per seat of capacity, percent of ticket revenue, percent ofanother schedule item's cash flow, etc.). To flexibly representdifferent types of real-world cost and income items, optionally severaldifferent rule types are supported. A positive rule input value denotespositive cash flow (income), while a negative rule input value denotesnegative cash flow (cost).

Schedule items are assigned to categories. These categories are used tofilter, or limit, the items displayed in a schedule, or to target a cashflow optimization to a single category. Categories for cash flowschedule items can be changed by clicking on an existing item'scategory, and selecting a new category from a drop down menu orotherwise.

Estimated cash flows are optionally provided for one or more items basedon pessimistic demand estimates, likely demand estimates, and optimisticdemand estimates. The pessimistic, likely, and optimistic estimatedtotal net cash flows are provided by summing the corresponding item cashflows. When the user activates the “optimize cash flow” control, theuser will enter parameters, and based on the cash flow schedule and theuser defined parameters, the system will select a ticket price for oneor more event areas or price bands that will result in the highestestimated cash flow.

The example table below provides example rule types, example cash flowcomputation methodologies, “what if” parameters, and applicable scheduleitems.

How Cash Flow How Cash Flow is “What-if” is Computed Computed (ruleParameters Cash (single rule inputs split by price Flow Depends ExampleSchedule Rule Type input) level) Upon Items (Applicability) Fixed Sameas rule Sum of rule inputs Rule input Flat fee for Amount input value.values for each price value. janitorial level. services. Fixed cost torent the venue. Fixed cost to pay the performer. Fixed advertisementincome. Fixed costs for promotions. Per Ticket Multiply the ruleMultiply the rule input Rule input Parking income. Sold (per input valueby value for each price value. Security attendee) overall number levelby the number of Ticket services where of tickets tickets sold in thatprice pricing staffing scales (estimated to be) level, then sum theseCapacity with the number sold. values together. levels of people Demandattending the estimates event. Janitorial services, where the fee scaleswith the number of “seats to clean”. Per Seat of Multiply rule Multiplythe rule input Rule input Capacity input value by value for each pricevalue. overall venue level by the number of Capacity capacity (shownseats of capacity for levels in capacity shift that price level, thenform). sum the per-price-level values together. Percent of Multiply therule Multiply the rule input Rule input Ticketing Ticket input value byvalue for each price value. Surcharges. Revenue the overall level by theexpected Ticket Performer's cut expected revenue for that price pricingof revenue. revenue. level, then sum the per- Capacity price levelvalues levels together. Demand estimates Percent of Multiply the ruleMultiply the rule input Rule input Performer's another input value byvalue by the overall value share of schedule the overall positive ornegative What the concessions items cash positive or cash flow foranother ‘linked to’ income. Flow negative cash item's cash flow and ifitem flow for another appropriate, for each depends item's cash flow.price level, where the upon per-price level values are summed

FIG. 24 illustrates an example cash flow enhancement user interfacewhich the user can utilize to specify cash flow enhancement oroptimization parameters. For example, the user can select a cash flowenhancement goal (e.g., minimize net cash flow or maximize net cashflow). The user can select a demand scenario (e.g., a pessimisticscenario based on pessimistic demand estimates, a likely scenario basedon likely demand estimates, or a optimistic scenario based on optimisticdemand estimates) that the cash flow is to be optimized or enhanced for.The user can further specify that the optimization or enhancement is tobe performed for all schedule items, or a subset thereof, such as itemsselected by a filter. The user can then activate an “Apply Optimization”control, and the software will perform a cash flow optimization orenhancement process in accordance with the user-specified parameters.The user can activate a “show ticket pricing” and the ticket prices thatare predicted to optimize the cash flow according to the user-specifiedparameters will be calculated and displayed.

FIG. 25 illustrates an example user interface for displaying demandestimates. The user interface displays estimated demand (e.g., thenumber of tickets that are estimated will be purchased) vs. ticketprices for one or more scenarios (e.g., a pessimistic scenario based onpessimistic demand estimates, a likely scenario based on likely demandestimates, or a optimistic scenario based on optimistic demandestimates). The user interface provides controls via which the user canselect various methods to shape and scale the demand vs. price curvesfor the currently selected price level and estimation source (e.g., via“Demand Decay”, “Scale by Percent”, and “Fit Curve” tabs).

The tabs provide a different way to shape or scale the demand vs. pricecurve for the currently selected price level and estimation source. Forexample, the Demand Decay tab will perform an exponential fit of theestimates or guesses. The “Scale by Percent” tab enables the user toscale the curves by percent. The “Fit Curve” tab enables the user toinstruct the system to perform a second order polynomial fit of theestimates or guesses. The “Manual Guesses” tab enables the user to adddemand guesses (e.g., pessimistic, likely, and optimistic guesses) for agiven ticket price (e.g., $40 in this example). Rather than having toprovide a guess for each dollar increment, the user can specify howdemand is to be scaled between guesses (e.g., scale demand by percent)at a given ticket price increment. The user can also specify at whatticket price that next price level should be used.

The user can select one or more demand estimation sources (e.g.,accounting department, box office, general manager, etc.) which providedemand estimates at one or more select ticket price levels (e.g., PL 1or PL 2). The user can make the selections by clicking on a row in thetable of estimation sources and price levels.

Demand estimates are completed by adding enough demand guesses toapproximate demand for prices between the minimum and maximum ticketprices (e.g., provided by a user while creating a new cash flowanalysis). In this regard, completed demand estimates for a given pricelevel are color coded or otherwise emphasized. In the illustratedexample “PL 1” is complete, but “PL 2” is incomplete. The demand guessesmatrix tabulates the various ticket demand guesses at various pricepoints.

When the user moves the cursor over the graph, the correspondingpessimistic, likely, and optimistic demand approximations (including,but not limited to guesses) for the corresponding ticket price aredisplayed in the corresponding fields (e.g., the “Price” field, the“Pessimistic” field, the “Likely” field, and the “Optimistic” field”).The resulting estimates can be weighted and combined to produce anotherestimated demand vs. ticket price curve.

FIG. 26 illustrates an example user interface for shifting seatingcapacity between price or minimum bid levels. The user interface enablesthe user to shift seating capacity from one price level to another pricelevel so as to enhance cash flow. In this example, the user can selectfrom two different price levels (PL 1 and PL 2) and can shift capacitiesbetween the price levels. Once the price levels are selected, thecapacity fields or the slider can be used to shift capacity between theprice levels. Price level fields can be used to select and display theprice levels being used. Corresponding capacity fields are used to enterand/or display the current capacity assigned to each price level. Avenue capacity field displays the venue capacity.

A set of per-price level capacity levels can also be saved as a preset.This can be useful in situations where a venue has fixed, “natural”divisions between price levels (e.g., lower and upper deck of atheater). Activation of a “Save” control will create a new capacitylevel preset, based upon the currently displayed capacities. Presetcapacity levels can be loaded by double-clicking on the preset's name,or by single-clicking on the preset name, then pressing the “Load”button.

Using one or more of the processes and apparatus disclosed herein,ticket prices can be set closer to market value, thereby ensuring fairerpricing and reducing the ability of ticket brokers and scalpers fromexploiting the traditional disparity between ticket face value andmarket value, wherein ticket brokers and scalpers purchase ticketsbefore most consumers attempt to, and then resell the tickets far aboveface value. Thus, increased fairness and efficiency is brought to theticket process.

While the foregoing detailed description discloses several embodimentsof the present invention, it should be understood that this disclosureis illustrative only and is not limiting of the present invention. Itshould be appreciated that the specific configurations and operationsdisclosed can differ from those described above, and that the methodsdescribed herein can be used in contexts other than ticketing systems.

We claim:
 1. A method of setting a ticket price, the method comprising:storing in computer readable memory a first size estimate correspondingto a first set of potential event attendees, wherein members of thefirst set are characterized by a first level of income; storing incomputer readable memory a second size estimate corresponding to asecond set of potential event attendees, wherein members of the secondset are characterized by a second level of income; storing in computerreadable memory a first value providing a first indication as to anevent ticket price for at least a portion of members of the first set,the first set characterized by the first level of income, wherein thefirst value is at least partly based on a survey; storing in computerreadable memory a second value providing a second indication as to anevent ticket price for at least a portion of members of the second set,the second set characterized by the second level of income, wherein thesecond value is at least partly based on the survey; accessing frommemory, via a system including one or more processors, the first sizeestimate, the second size estimate, the first value, and the secondvalue; accessing historical ticket sales data; and based at least inpart on the historical data, the first size estimate corresponding tothe first set of potential event attendees, wherein members of the firstset are characterized by the first level of income, the second sizeestimate corresponding to the second set of potential event attendees,wherein members of the second set are characterized by the second levelof income, the first value providing the first indication, and thesecond value providing the second indication, setting, by said one ormore processors, at least a first event ticket price for a first set oftickets prior to offering the first set of tickets for sale.
 2. Themethod as defined in claim 1, wherein members of the first set ofpotential event attendees are associated with a first motivation forattending the event.
 3. The method as defined in claim 1, whereininformation accessed from memory that indicates how many potential eventattendees are likely to purchase expensive event seats to impress othersis used in setting at least the first event ticket price.
 4. The methodas defined in claim 1, wherein information accessed from memory thatindicates how many potential event attendees are likely to attend theevent with the intention of socializing with others is used in settingat least the first event ticket price.
 5. The method as defined in claim1, wherein information accessed from memory that indicates how manypotential event attendees are likely to only purchase inexpensive eventtickets.
 6. The method as defined in claim 1, wherein the members of thefirst set of potential event attendees have high incomes or a relativelylarge amount of disposable income as compared to other potentialattendees, or high incomes and a relatively large amount of disposableincome as compared to other potential attendees.
 7. The method asdefined in claim 1, wherein the members of the first set of potentialevent attendees have low incomes as compared to other potentialattendees.
 8. The method as defined in claim 1, wherein members of thefirst set of potential event attendees include ticket brokers thatresell tickets.
 9. The method as defined in claim 1, the method furthercomprising: accessing a first parameter associated with patience withrespect to ticket acquisition associated with members of the first setof potential event attendees, wherein the first event ticket price isset partly based on the first parameter.
 10. The method as defined inclaim 1, the method further comprising: reading a first limit on howmany event tickets a user can purchase, wherein the first event ticketprice is set partly based on the first limit.
 11. The method as definedin claim 1, the method further comprising: reading a first minimumpermissible event ticket price, wherein the first event ticket price isset partly based on the first minimum permissible ticket price.
 12. Themethod as defined in claim 1, the method further comprising: accessingevent cost data, wherein the first event ticket price is set partlybased on the event cost data.
 13. The method as defined in claim 1, themethod further comprising: accessing historical ticket sales datarelated to resold tickets, wherein the first event ticket price is setpartly based on the historical ticket sales data related to resoldtickets.
 14. The method as defined in claim 1, the method furthercomprising: accessing from memory a median ticket price at which it isestimated that at least a first portion of the first set of potentialevent attendees would be willing to pay, wherein the first event ticketprice is set partly based on the median ticket price.
 15. The method asdefined in claim 1, the method further comprising: accessing historicalsales data related to concessions sold at least one event, wherein thefirst event ticket price is set partly based on the historical salesdata related to concessions.
 16. The method as defined in claim 1, themethod further comprising: accessing historical sales data related toevent income, wherein the first event ticket price is set partly basedon the historical income data.
 17. The method as defined in claim 1, themethod further comprising setting the first event ticket price is setpartly based on the historical sales data related to concessions. 18.The method as defined in claim 1, the method further comprising settingticket prices for a plurality of venue areas at least partly based onhistorical sales data related to concessions and tickets.
 19. The methodas defined in claim 1, the method further comprising accessing cost datarelated to putting on the event, wherein the first event ticket price isset partly based on the cost data.
 20. A system, comprising: a computersystem including one or more processors; computer readable memory thatstores: historical ticket sales data; a first size estimate, wherein thefirst size estimate corresponds to a first set of potential eventattendees, wherein members of the first set are characterized by a firstlevel of income; a second size estimate, wherein the second sizeestimate corresponds to a second set of potential event attendees,wherein members of the second set are characterized by a second level ofincome; a first value providing a first indication as to an event ticketprice for at least a subset of members of the first set, the first setcharacterized by the first level of income, wherein the first value isat least partly based on a survey; a second value providing a secondindication as to an event ticket price for at least a subset of membersof the second set, the second set characterized by the second level ofincome, wherein the second value is at least partly based on the survey;and code stored in computer readable memory, that when executed by thecomputer system is configured to at least partly set a first eventticket price for a plurality of tickets prior to offering the pluralityof tickets for sale, using: the historical ticket sales data; the firstsize estimate corresponding to the first set of potential eventattendees, wherein members of the first set are characterized by thefirst level of income, the second size estimate corresponding to thesecond set of potential event attendees, wherein members of the secondset are characterized by the second level of income, the first valueproviding the first indication, and the second value providing thesecond indication.
 21. The system as defined in claim 20, whereinmembers of the first set are identified via an indication stored inmemory as being associated with a first identified motivation forattending the event, wherein the code is configured to at least partlyset, based in part on the first identified motivation indication, atleast one event ticket price.
 22. The system as defined in claim 20,wherein members of the first set are identified via an indication storedin memory as being more likely to purchase expensive event seats toimpress others relative to members of at least one other set, whereinthe code is configured to at least partly set, based in part on the morelikely to purchase expensive event seats to impress others indication,at least one event ticket price.
 23. The system as defined in claim 20,wherein members of the first set are identified via an indication storedin memory as being more likely to attend the event with the intention ofsocializing with others relative to members of at least one other set,wherein the code is configured to at least partly set, based in part onthe more likely to attend the event with the intention of socializingwith others indication, at least one event ticket price.
 24. The systemas defined in claim 20, wherein members of the first set are identifiedvia an indication stored in memory as being likely to only purchaseinexpensive event tickets, wherein the code is configured to at leastpartly set, based in part on the more likely to only purchaseinexpensive event tickets indication, at least one event ticket price.25. The system as defined in claim 20, wherein the members of the firstset are identified via an indication stored in memory as: (a) havinghigh incomes, or (b) a large amount of disposable income, or both (a)and (b) wherein the code is configured to at least partly set, based inpart on (a) or (b), or (a) and (b), at least one event ticket price. 26.The system as defined in claim 20, wherein the members of the first setare identified via an indication stored in memory as having low incomes,wherein the code is configured to at least partly set, based in part onthe low income indication, at least one event ticket price.
 27. Thesystem as defined in claim 20, wherein members of the first set includemembers identified via an indication stored in memory as ticket brokersthat resell tickets, wherein the code is configured to at least partlyset, based in part on the ticket brokers indication, at least one eventticket price.
 28. The system as defined in claim 20, wherein the code isconfigured to: access a first parameter associated with patience withrespect to ticket acquisition associated with members of the first set,wherein the first parameter is related to a probability of an individualattempting another transaction if a first transaction fails because aprice associated with a ticket presented to the individual is too highfor the individual; and at least partly set the first event ticket priceat least partly based on the first parameter.
 29. The system as definedin claim 28, wherein the first parameter is a geometrically timedecaying parameter.
 30. The system as defined in claim 20, wherein thecode is configured to: read a first limit on how many event tickets auser can purchase, wherein the first event ticket price is set partlybased on the first limit.
 31. The system as defined in claim 20, whereinthe code is configured to: access historical ticket sales data relatedto resold tickets, wherein the first event ticket price is set partlybased on the historical ticket sales data related to resold tickets. 32.The system as defined in claim 20, wherein the code is configured to:access from memory a median ticket price that it is estimated that atleast a first portion of the first set would be willing to pay, whereinthe first event ticket price is set partly based on the median ticketprice.
 33. The system as defined in claim 20, wherein in the code isconfigured to at least partly set at least one ticket price using: aspecified first minimum permissible event ticket price; historicalticket sales data related to resold tickets for one or more past events;historical sales data related to event income, including: historicalsales data related to concessions; historical sales data related totickets; and historical ticket sales data related to sales of tickets toticket brokers that resell tickets.
 34. The system as defined in claim20, wherein the code is configured to: access historical sales datarelated to concessions sold at least one event, wherein the first eventticket price is set partly based on the historical sales data related toconcessions.
 35. The system as defined in claim 20 wherein the code isconfigured to: access historical sales data related to event income,wherein the first event ticket price is set partly based on thehistorical income data, including at least historical income datarelated to concessions and parking.
 36. The system as defined in claim20, wherein the code is configured to at least partly set ticket pricesfor a plurality of venue areas, wherein the set tickets prices do notmaximize revenues from ticket sales.